Data structures and delivery methods for expediting virtual reality playback

ABSTRACT

A video stream for a scene for a virtual reality or augmented reality experience may be stored and delivered to a viewer. The video stream may be divided into a plurality of units based on time segmentation, viewpoint segmentation, and/or view orientation segmentation. Each of the units may be divided into a plurality of sub-units based on a different segmentation from the units, via time segmentation, viewpoint segmentation, and/or view orientation segmentation. At least a portion of the video stream may be stored in a file that includes a plurality of the units. Each unit may be a group of pictures that is a sequence of successive frames in time. Each sub-unit may be a vantage defining a viewpoint from which the scene is viewable. Each vantage may be further divided into tiles, each of which is part of the vantage, limited to one or more particular view orientations.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of U.S. applicationSer. No. 15/590,877 for “Spatial Random Access Enabled Video System witha Three-Dimensional Viewing Volume”, filed May 9, 2017, the disclosureof which is incorporated herein by reference.

U.S. application Ser. No. 15/590,877 is a continuation-in-part of U.S.application Ser. No. 15/084,326 for “Capturing Light-Field Volume Imageand Video Data Using Tiled Light-Field Cameras”, filed Mar. 29, 2016,the disclosure of which is incorporated herein by reference in itsentirety.

U.S. patent application Ser. No. 15/084,326 claims the benefit of U.S.Provisional Application Ser. No. 62/148,055 for “Light Guided ImagePlane Tiled Arrays with Dense Fiber Optic Bundles for Light-Field andHigh Resolution Image Acquisition”, filed Apr. 15, 2015, the disclosureof which is incorporated herein by reference in its entirety.

U.S. patent application Ser. No. 15/084,326 also claims the benefit ofU.S. Provisional Application Ser. No. 62/148,460 for “Capturing LightField Volume Image and Video Data Using Tiled Light Field Cameras”,filed Apr. 16, 2015, the disclosure of which is incorporated herein byreference in its entirety.

The present application is also a continuation-in-part of U.S.application Ser. No. 15/590,808 for “Adaptive Control for ImmersiveExperience Delivery,”, filed May 9, 2017, the disclosure of which isincorporated herein by reference.

The present application is also related to U.S. patent application Ser.No. 14/302,826 for “Depth Determination for Light Field Images”, filedJun. 12, 2014 and issued as U.S. Pat. No. 8,988,317 on Mar. 24, 2015,the disclosure of which is incorporated herein by reference.

The present application is also related to U.S. application Ser. No.15/590,841 for “Vantage Generation and Interactive Playback,”, filed May9, 2017, the disclosure of which is incorporated herein by reference.

The present application is also related to U.S. application Ser. No.15/590,951 for “Wedge-Based Light-Field Video Capture,”, filed May 9,2017, the disclosure of which is incorporated herein by reference.

TECHNICAL FIELD

The present document relates to the display of video from user-selectedviewpoints for use in virtual reality, augmented reality, free-viewpointvideo, omnidirectional video, and/or the like.

BACKGROUND

Display of a volume of captured video or positional tracking video mayenable a viewer to perceive a captured scene from any location and atany viewing angle within a viewing volume. Using the data provided bysuch a video system, a viewpoint can be reconstructed to provide fullmotion parallax and/or correct view-dependent lighting. When viewingthis video with a virtual reality head-mounted display, the user mayenjoy an immersive virtual presence within an environment. Such avirtual reality experience may provide six degrees of freedom anduncompromised stereoscopic perception at any interpupillary distance.

One key challenge to video with a three-dimensional viewing volume isthe need for random access to portions of the video stream. Since theparticular view to be rendered is user-determined in real-time, therelevant portions of the data must be rapidly located, accessed,decompressed, and/or delivered to the viewer. Known video storagemethods and schemes are generally ill-suited to providing such random,real-time access.

SUMMARY

Numerous data representations are possible for six degree-of-freedom,full parallax VR video. Digital sampling may first be carried out, ofall view-dependent color and depth information of any visible surfacesin a given viewing volume. Such sampling representation may providesufficient data to render any arbitrary viewpoints in the 3D viewingspace. The viewer may desirably be able to enjoy smooth view-dependentlighting transition and artifacts-free occlusion filling when switchingbetween different viewpoints.

These objectives may be obtained, in some instances, by utilizingvantage-based representation to store all video information and rendersof any arbitrary field of view (FoV) inside a 3D viewing volume.Specifically, a 3D sampling grid may be created over the viewing volume.Each point of the sampling grid may be called a “vantage.” Each vantagemay contain a 360°/180° projected view of the scene at a givencoordinate in the sampling grid. It may contain color, texture, and/ordepth information. To provide smooth transitions to view-dependentlighting and rendering, the system may perform a barycentricinterpolation of color between four of the vantages whose locations forma tetrahedron that includes the view position for each eye view.

The vantage-based system described above may facilitate real-timeplayback and random access for 6 degree-of-freedom virtual reality oraugmented reality viewing. During decoding and rendering, a light fieldvirtual reality or augmented reality system may load and render multiplevantages in real-time at high frame rate. A compressed vantage, whichrequires a decoding procedure, may strain the computational, memory, andbandwidth resources of the client's system. To relieve this pressure, inat least one embodiment, the system may only decode and render theregion of vantages within a viewer's field of view inside the 3D viewingvolume. This may be enabled by dividing each vantage into multipletiles. Each tile may be independently or jointly encoded with thesystem's vantage encoder using image- and/or video-based compressiontechniques. When a viewer is accessing an arbitrary viewpoint inside aviewing volume, the playback system may find the corresponding vantageswithin the sampling grid and fetch the corresponding tiles inside thevantages.

To provide quick spatial and temporal random access to each tile,hierarchical address lookup tables may be built during encapsulation ofthe video content. The video stream may first be broken into groups ofpictures (GOPs), each of which contains a sequence of successive framesin time. In each GOP, the content may be grouped into vantages, witheach vantage containing a sequence of video frames, and each framecontaining all the tiles of the vantage.

The hierarchical address lookup may be organized according to the samemanner:

-   -   1. A GOP address offset table to store the start address of each        GOP;    -   2. A vantage-relative address offset table that denotes the        offset location of each vantage-relative to the start address of        the corresponding GOP payload; and    -   3. A tile-relative address offset table that informs the offset        address and the data size of each tile.

By providing these address tables, the playback client may quicklyaccess and fetch the relevant tile from its file system and memorywithout any lookahead. When temporal or inter-vantage dependencies areused, the client may quickly use the lookup table to find and decode itsreferences, as well as to supplement any caching strategies that may beused at the client. Therefore, input and/or output between disk andmemory, which is often a critical bottleneck for a virtualreality/augmented reality playback system, is minimized.

The video content, audio content, and address tables may be incorporatedinto a single file in which the address tables can easily be used tolocate the relevant portions of the audio and video content. In additionto video and audio data, parameters such as scene parameters, renderingparameters, vantage configurations, and viewing behavior may bespecified within the file. These parameters may be used by the playbackclient to generate an immersive virtual reality or augmented experiencematched to the real-time viewpoint and view orientation of the viewer.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate several embodiments. Together withthe description, they serve to explain the principles of theembodiments. One skilled in the art will recognize that the particularembodiments illustrated in the drawings are merely exemplary, and arenot intended to limit scope.

FIG. 1 is a diagram of a plenoptic light-field camera, according to oneembodiment.

FIG. 2 is a conceptual diagram of a light-field volume, according to oneembodiment.

FIG. 3 is a conceptual diagram of virtual viewpoint generation from afully sampled light-field volume.

FIG. 4 is a conceptual diagram comparing the sizes of a physical capturedevice, capturing all incoming rays within a limited field-of-view, andthe virtual size of the fully sampled light-field volume, according toone embodiment.

FIG. 5 is a conceptual diagram of a coordinate system for a light-fieldvolume.

FIG. 6 is a diagram of an array light-field camera, according to oneembodiment.

FIG. 7 is a diagram of a virtual reality capture system according to theprior art, developed by Jaunt.

FIG. 8 is a diagram of a stereo virtual reality capture system accordingto the prior art.

FIG. 9 is a block diagram depicting a capture system according to oneembodiment.

FIG. 10 is a diagram showing a tiled array in an ideal ringconfiguration of contiguous plenoptic light-field cameras, according toone embodiment.

FIGS. 11A through 11C are diagrams showing various patterns for joiningcamera lenses to create a continuous surface on a volume of space,according to various embodiments.

FIG. 12 is a diagram of a ring configuration with the addition of atop-facing light-field camera, according to one embodiment.

FIG. 13 is a diagram showing different basic lens designs that can beused in different embodiments, and shows typical field-of-view (FOV) andNumerical Apertures for those designs.

FIG. 14 is an exemplary schematic cross section diagram of a doubleGauss lens design that can be used in one embodiment.

FIG. 15 is a diagram showing ring configuration of plenoptic light-fieldcameras with circular lenses and non-contiguous entrance pupils,according to one embodiment.

FIGS. 16A through 16C are diagrams depicting a sparsely populatedlight-field ring configuration that rotates, according to oneembodiment.

FIGS. 17A through 17C are diagrams depicting a fully populated set oflenses and sparsely populated sensors, according to one embodiment.

FIGS. 18A through 18C are diagrams of a fully populated set of lensesand sparsely populated sensors, according to one embodiment.

FIG. 19 is a diagram showing a ring configuration of contiguous arraylight-field cameras, according to one embodiment.

FIGS. 20A and 20B are diagrams of a fully populated set of objectivelens arrays and sparsely populated sensors for array light-fieldcameras, according to one embodiment.

FIG. 21 is a diagram showing an array light-field camera using a taperedfiber optic bundle, according to one embodiment.

FIG. 22 is a diagram showing array light-field cameras using taperedfiber optic bundles in a ring configuration, according to oneembodiment.

FIG. 23 is a diagram showing a tiled light-field camera array in asingle layer ring configuration, according to one embodiment.

FIG. 24 is a diagram showing a tiled light-field camera array in a duallayer ring configuration, according to one embodiment.

FIGS. 25A through 25B are diagrams comparing a schematic view of aplenoptic light-field camera to a virtual camera array that isapproximately optically equivalent.

FIG. 26 is a diagram showing a possible set of two cylindricalcalibration charts that may be used to calibrate a tiled light-fieldcamera array, according to one embodiment.

FIG. 27 is an image of an example of a virtual reality headset, theOculus Rift (Development Kit version).

FIG. 28 is a conceptual drawing showing a virtual camera system andfield-of-view that may be used to generate virtual views, according toone embodiment.

FIG. 29 is a conceptual drawing showing a coordinate system with avirtual camera system based on an ideal lens, according to oneembodiment.

FIG. 30 is a conceptual drawing showing a virtual camera system based ona more complete model of a virtual lens, according to one embodiment.

FIG. 31 is a diagram showing example output from an optical ray tracer,according to one embodiment.

FIGS. 32A through 32C are conceptual diagrams showing a rotatingsparsely populated tiled array of array light-field cameras, accordingto one embodiment.

FIG. 33 is an exemplary image showing a CMOS photosensor mounted in anelectronics package.

FIG. 34 is a diagram showing the relationship between the physical sizeand field-of-view on the capture surface to the size of a virtual fullysampled light-field volume, according to one embodiment.

FIGS. 35A through 35D are perspective and side elevation views depictinga tiled array of conventional cameras, according to one embodiment.

FIG. 36 is a diagram that depicts stitching that may be used to providean extended vertical field-of-view.

FIG. 37 is a perspective view depicting a tiled array according toanother alternative embodiment.

FIG. 38 depicts a tiling scheme representing some or all of the viewencoded in the video data for a single vantage, including three layers,according to one embodiment.

FIG. 39 depicts an encoder according to one embodiment.

FIGS. 40 through 44 depict various vantage encoding schemes according tocertain embodiments.

FIGS. 45A and 45B depict encoding schemes with inter-vantage prediction,according to certain alternative embodiments.

FIGS. 46A and 46B depict encoding schemes according to furtheralternative embodiments.

FIG. 47 depicts a system for generating and compressing tiles, accordingto one embodiment.

FIG. 48 depicts a system for tile decoding, compositing, and playback,according to one embodiment.

FIG. 49 is a diagram depicting how a vantage view may be composed,according to one embodiment.

FIG. 50 depicts the view of a checkerboard pattern from a known virtualreality headset.

FIG. 51 depicts a method for capturing volumetric video data, encodingthe volumetric video data, decoding to obtain viewpoint video data, anddisplaying the viewpoint video data for a viewer, according to oneembodiment.

FIG. 52 is a series of graphs depict a tile-based scheme, according toone embodiment.

FIGS. 53A and 53B depict exemplary tiling schemes, according to certainembodiments.

FIG. 54 depicts a hierarchical coding scheme, according to oneembodiment.

FIGS. 55A, 55B, 55C, and 55D are a series of views depicting theoperation of the hierarchical coding scheme of FIG. 54 in twodimensions, according to one embodiment.

FIGS. 56A, 56B, 56C, and 56D are a series of views depicting theoperation of the hierarchical coding scheme of FIG. 54 in threedimensions, according to another embodiment.

FIGS. 57A, 57B, 57C, and 57D are a series of graphs depicting theprojection of depth layers onto planar image from a spherical viewingrange from a vantage, according to one embodiment.

FIG. 58 is a flow diagram depicting a method for storing a video stream,which may be volumetric video data to be used for a virtual reality oraugmented reality experience, according to one embodiment.

FIG. 59 depicts a file that may be used to store audio and/or video datafor a virtual reality or augmented reality experience, according to oneembodiment.

FIG. 60 is a representation of a bitstream that may be used for thevideo payload, according to one embodiment.

FIG. 61 is a representation of a bitstream that may be used for thevideo payload, according to another embodiment.

FIGS. 62A, 62B, and 62C depict a GOP offset table, a vantage offsettable, and a tile offset table, respectively, according to oneembodiment.

FIGS. 63A and 63B depict file generation tools, according to certainembodiments.

DETAILED DESCRIPTION

Multiple methods for capturing image and/or video data in a light-fieldvolume and creating virtual views from such data are described. Thedescribed embodiments may provide for capturing continuous or nearlycontinuous light-field data from many or all directions facing away fromthe capture system, which may enable the generation of virtual viewsthat are more accurate and/or allow viewers greater viewing freedom.

Definitions

For purposes of the description provided herein, the followingdefinitions are used:

-   -   Active area: the portion of a module that receives light to be        provided as image data by the module.    -   Array light-field camera: a type of light-field camera that        contains an array of objective lenses with overlapping        fields-of-view and one or more photosensors, with the viewpoint        from each objective lens captured as a separate image.    -   Capture surface, or “physical capture surface”: a surface        defined by a tiled array of light-field cameras, at which light        is received from an environment into the light-field cameras,        with exemplary capture surfaces having cylindrical, spherical,        cubic, and/or other shapes.    -   Capture system: a tiled array of light-field cameras used to        fully or sparsely capture a light-field volume.    -   Client computing device: a computing device that works in        conjunction with a server such that data is exchanged between        the client computing device and the server.    -   Computing device: any device having a processor.    -   Conventional image: an image in which the pixel values are not,        collectively or individually, indicative of the angle of        incidence at which light is received on the surface of the        sensor.    -   Data store: a repository of data, which may be at a single        location or distributed over multiple locations, and may be        provided through the use of any volatile or nonvolatile data        storage technologies.    -   Depth: a representation of distance between an object and/or        corresponding image sample and the entrance pupil of the optics        of the capture system.    -   Disk: a region in a light-field image that is illuminated by        light passing through a single microlens; may be circular or any        other suitable shape.    -   Disk image: a single image of the aperture stop, viewed through        a plenoptic microlens, and captured by a region on the sensor        surface.    -   Display device: a device such as a video screen that can display        images and/or video for a viewer.    -   Entrance pupil: the optical image of the physical aperture stop,        as “seen” through the front of the lens system, with a geometric        size, location, and angular acceptance acting as the camera's        window of view into an environment.    -   Environment: a real-world scene to be captured for subsequent        visualization.    -   Fiber optic bundle: a set of aligned optical fibers capable of        transmitting light.    -   Frame: a single image of a plurality of images or a video        stream.    -   Free-viewpoint video: video that changes in response to altering        the viewpoint of the viewer    -   Fully sampled light-field volume: a light-field volume that has        been captured in a manner inclusive of ray data from all        directions at any location within the light-field volume,        enabling the generation of virtual views from any viewpoint, at        any orientation, and with any field-of-view.    -   Image: a two-dimensional array of pixel values, or pixels, each        specifying a color.    -   Input device: any device that receives input from a user.    -   Layer: a segment of data, which may be stored in conjunction        with other layers pertaining to common subject matter such as        the video data for a particular vantage.    -   Leading end: the end of a fiber optic bundle that receives        light.    -   Light-field camera: any camera capable of capturing light-field        images.    -   Light-field coordinate: for a single light-field camera, the        four-dimensional coordinate (for example, x, y, u, v) used to        index a light-field sample captured by a light-field camera, in        which (x, y) may be the spatial coordinate representing the        intersection point of a light ray with a microlens array, and        (u, v) may be the angular coordinate representing an        intersection point of the light ray with an aperture plane.    -   Light-field data: data indicative of the angle of incidence at        which light is received on the surface of the sensor.    -   Light-field image: an image that contains a representation of        light-field data captured at the sensor, which may be a        four-dimensional sample representing information carried by ray        bundles received by a single light-field camera.    -   Light-field volume: the combination of all light-field images        that represents, either fully or sparsely, light rays entering        the physical space defined by the light-field volume.    -   Light-field volume coordinate: for a capture system, an extended        version of light-field coordinates that may be used for        panoramic and/or omnidirectional viewing (for example, rho1,        theta1, rho2, theta2), in which (rho1, theta1) represent        intersection of a light ray with an inner sphere and (rho2,        theta2) represent intersection of the light ray with an outer        sphere concentric with the inner sphere.    -   Main lens, or “objective lens”: a lens or set of lenses that        directs light from a scene toward an image sensor.    -   Microlens: a small lens, typically one in an array of similar        microlenses.    -   Microlens array: an array of microlenses arranged in a        predetermined pattern.    -   Offset table: a table indicating the addresses of elements        within one or more files, for example, as an offset from a datum        such as the end of the offset table.    -   Omnidirectional stereo video: video in which the user selects a        fixed viewpoint from within a viewing volume.    -   Packaging: The housing, electronics, and any other components of        an image sensor that reside outside the active area.    -   Plenoptic light-field camera: a type of light-field camera that        employs a microlens-based approach in which a plenoptic        microlens array is positioned between the objective lens and the        photosensor.    -   Plenoptic microlens array: a microlens array in a plenoptic        camera that is used to capture directional information for        incoming light rays, with each microlens creating an image of        the aperture stop of the objective lens on the surface of the        image sensor.    -   Processor: any processing device capable of processing digital        data, which may be a microprocessor, ASIC, FPGA, or other type        of processing device.    -   Ray bundle, “ray,” or “bundle”: a set of light rays recorded in        aggregate by a single pixel in a photosensor.    -   Ring array: a tiled array of light-field cameras in which the        light-field cameras are generally radially symmetrically        arranged about an axis to define a cylindrical capture surface        of light-field cameras facing outward.    -   Scene: some or all of an environment that is to be viewed by a        viewer.    -   Sectoral portion: a portion of an arcuate or semispherical        shape; or in the case of a cylindrical or spherical mapping of        video data from a vantage or viewpoint, a portion of the mapping        of video data corresponding to a Field-of-View smaller than the        mapping.    -   Sensor, “photosensor,” or “image sensor”: a light detector in a        camera capable of generating images based on light received by        the sensor.    -   Spherical array: a tiled array of light-field cameras in which        the light-field cameras are generally arranged in a spherical        pattern to define a spherical capture surface of light-field        cameras facing outward.    -   Stereo virtual reality: an extended form of virtual reality in        which each eye is shown a different view of the virtual world,        enabling stereoscopic three-dimensional perception.    -   Subset: one or more, but not all, of a group of items.    -   Subview: the view or image from an individual view in a        light-field camera (a subaperture image in a plenoptic        light-field camera, or an image created by a single objective        lens in an objective lens array in an array light-field camera).    -   Sub-sub-unit: a subset of a sub-unit.    -   Sub-unit: a subset of a unit.    -   Tapered fiber optic bundle, or “taper”: a fiber optic bundle        that is larger at one end than at the other.    -   Tile: a portion of the view of a scene from a particular        viewpoint, pertaining to a particular range of view        orientations, i.e., a particular field of view, from that        viewpoint.    -   Tiled array: an arrangement of light-field cameras in which the        light-field cameras are compactly and/or loosely, evenly and/or        unevenly distributed about an axis and oriented generally        outward to capture an environment surrounding the tiled array,        with exemplary tiled arrays including ring-shaped arrays,        spherical arrays, cubic arrays, and the like.    -   Time segmentation: division of data into segments based on        applicable time frames, such as ranges of time within a video        stream.    -   Trailing end: the end of a fiber optic bundle that emits light.    -   Unit: a subset of a video stream.    -   Vantage: a pre-determined point within a viewing volume, having        associated video data that can be used to generate a view from a        viewpoint at the vantage.    -   Video data: data derived from image or video capture, associated        with a particular vantage or viewpoint.    -   View direction: a direction along which a scene is to be viewed        from a viewpoint; can be conceptualized as a vector extending        along the center of a Field-of-View from the viewpoint.    -   View orientation segmentation: division of data into segments        based on a range of view orientations along which a scene can be        viewed.    -   Viewpoint: a point from which an environment is to be viewed.    -   Viewpoint segmentation: division of data into segments based on        the viewpoint applicable to each segment of the data.    -   Viewpoint video data: video data associated with a particular        viewpoint that can be used to generate a view from that        viewpoint.    -   Virtual reality: an immersive viewing experience in which images        presented to the viewer are based on the location and/or        orientation of the viewer's head and/or eyes.    -   Virtual view: a reconstructed view, typically for display in a        virtual reality or augmented reality headset, which may be        generated by resampling and/or interpolating data from a        captured light-field volume.    -   Virtual viewpoint: the location, within a coordinate system        and/or light-field volume, from which a virtual view is        generated.    -   Volumetric video: image or video captured in a manner that        permits the video to be viewed from multiple viewpoints    -   Volumetric video data: data derived from image or video capture,        which can be used to construct a view from multiple viewpoints        within a viewing volume.

In addition, for ease of nomenclature, the term “camera” is used hereinto refer to an image capture device or other data acquisition device.Such a data acquisition device can be any device or system foracquiring, recording, measuring, estimating, determining and/orcomputing data representative of a scene, including but not limited totwo-dimensional image data, three-dimensional image data, and/orlight-field data. Such a data acquisition device may include optics,sensors, and image processing electronics for acquiring datarepresentative of a scene, using techniques that are well known in theart. One skilled in the art will recognize that many types of dataacquisition devices can be used in connection with the presentdisclosure, and that the disclosure is not limited to cameras. Thus, theuse of the term “camera” herein is intended to be illustrative andexemplary, but should not be considered to limit the scope of thedisclosure. Specifically, any use of such term herein should beconsidered to refer to any suitable device for acquiring image data.

In the following description, several techniques and methods forprocessing light-field images are described. One skilled in the art willrecognize that these various techniques and methods can be performedsingly and/or in any suitable combination with one another.

Problem Description

Virtual reality is intended to be a fully immersive experience forusers, often having the goal of creating an experience that is as closeas possible to “being there.” Users typically use headsets withimmersive, wide-angle stereo viewing, multidirectional sound, andonboard sensors that can measure orientation, accelerations, and/orposition. As an example, FIG. 27 shows an image of the Oculus RiftDevelopment Kit headset as an example of a virtual reality headset 2700.Viewers using virtual reality and/or augmented reality headsets may movetheir heads to point in any direction, move forward and backward, andmay move their heads side to side. The point of view from which the userviews his or her surroundings may change to match the motion of his orher head.

FIG. 27 depicts some exemplary components of the virtual reality headset2700. Specifically, the virtual reality headset 2700 may have aprocessor 2710, memory 2720, a data store 2730, user input 2740, and adisplay screen 2750. Each of these components may be any device known inthe computing and virtual reality arts for processing data, storing datafor short-term or long-term use, receiving user input, and displaying aview, respectively. In some embodiments, the user input 2740 may includeone or more sensors that detect the position and/or orientation of thevirtual reality headset 2700. By maneuvering his or her head, a user(i.e., a “viewer”) may select the viewpoint and/or view direction fromwhich he or she is to view an environment.

The virtual reality headset 2700 may also have additional components notshown in FIG. 27. Further, the virtual reality headset 2700 may bedesigned for standalone operation or operation in conjunction with aserver that supplies video data, audio data, and/or other data to thevirtual reality headset. Thus, the virtual reality headset 2700 mayoperate as a client computing device. As another alternative, any of thecomponents shown in FIG. 27 may be distributed between the virtualreality headset 2700 and a nearby computing device such that the virtualreality headset 2700 and the nearby computing device, in combination,define a client computing device.

Virtual reality content may be roughly divided into two segments:synthetic content and real world content. Synthetic content may includeapplications like video games or computer-animated movies that aregenerated by the computer. Real world content may include panoramicimagery and/or live action video that is captured from real places orevents.

Synthetic content may contain and/or be generated from a 3-dimensionalmodel of the environment, which may be also used to provide views thatare matched to the actions of the viewer. This may include changing theviews to account for head orientation and/or position, and may eveninclude adjusting for differing distances between the eyes.

Real world content is more difficult to fully capture with known systemsand methods, and is fundamentally limited by the hardware setup used tocapture the content. FIGS. 7 and 8 show exemplary capture systems 700and 800, respectively. Specifically, FIG. 7 depicts a virtual realitycapture system, or capture system 700, according to the prior art,developed by Jaunt. The capture system 700 consists of a number oftraditional video capture cameras 710 arranged spherically. Thetraditional video capture cameras 710 are arranged facing outward fromthe surface of the sphere. FIG. 8 depicts a stereo virtual realitycapture system, or capture system 800, according to the prior art. Thecapture system 800 consists of 8 stereo camera pairs 810, plus onevertically facing camera 820. Image and/or video data is captured fromthe camera pairs 810, which are arranged facing outward from a ring. Inthe capture system 700 and the capture system 800, the image and/orvideo data captured is limited to the set of viewpoints in the cameraarrays.

When viewing real world content captured using these types of systems, aviewer may only be viewing the captured scene with accuracy whenvirtually looking out from one of the camera viewpoints that has beencaptured. If the viewer views from a position that is between cameras,an intermediate viewpoint must be generated in some manner. There aremany approaches that may be taken in order to generate theseintermediate viewpoints, but all have significant limitations.

One method of generating intermediate viewpoints is to generate two 360°spherically mapped environments—one for each eye. As the viewer turnshis or her head, each eye sees a window into these environments. Imageand/or video data from the cameras in the array are stitched onto thespherical surfaces. However, this approach is geometrically flawed, asthe center of perspective for each eye changes as the user moves his orher head, and the spherical mapping assumes a single point of view. As aresult, stitching artifacts and/or geometric distortions cannot be fullyavoided. In addition, the approach can only reasonably accommodateviewers changing their viewing direction, and does not perform well whenthe user moves his or her head laterally, forward, or backward.

Another method to generate intermediate viewpoints is to attempt togenerate a 3D model from the captured data, and interpolate betweenviewpoints based at least partially on the generated model. This modelmay be used to allow for greater freedom of movement, but isfundamentally limited by the quality of the generated three-dimensionalmodel. Certain optical aspects, like specular reflections, partiallytransparent surfaces, very thin features, and occluded imagery areextremely difficult to correctly model. Further, the visual success ofthis type of approach is highly dependent on the amount of interpolationthat is required. If the distances are very small, this type ofinterpolation may work acceptably well for some content. As themagnitude of the interpolation grows (for example, as the physicaldistance between cameras increases), any errors will become morevisually obvious.

Another method of generating intermediate viewpoints involves includingmanual correction and/or artistry in the postproduction workflow. Whilemanual processes may be used to create or correct many types of issues,they are time intensive and costly.

A capture system that is able to capture a continuous or nearlycontinuous set of viewpoints may remove or greatly reduce theinterpolation required to generate arbitrary viewpoints. Thus, theviewer may have greater freedom of motion within a volume of space.

Tiled Array of Light-Field Cameras

The present document describes several arrangements and architecturesthat allow for capturing light-field volume data from continuous ornearly continuous viewpoints. The viewpoints may be arranged to cover asurface or a volume using tiled arrays of light-field cameras. Suchsystems may be referred to as “capture systems” in this document. Atiled array of light-field cameras may be joined and arranged in orderto create a continuous or nearly continuous light-field capture surface.This continuous capture surface may capture a light-field volume. Thetiled array may be used to create a capture surface of any suitableshape and size.

FIG. 2 shows a conceptual diagram of a light-field volume 200, accordingto one embodiment. In FIG. 2, the light-field volume 200 may beconsidered to be a spherical volume. Rays of light 210 originatingoutside of the light-field volume 200 and then intersecting with thelight-field volume 200 may have their color, intensity, intersectionlocation, and direction vector recorded. In a fully sampled light-fieldvolume, all rays and/or “ray bundles” that originate outside thelight-field volume are captured and recorded. In a partially sampledlight-field volume or a sparsely sampled light-field volume, a subset ofthe intersecting rays is recorded.

FIG. 3 shows a conceptual diagram of virtual viewpoints, or subviews300, that may be generated from captured light-field volume data, suchas that of the light-field volume 200 of FIG. 2. The light-field volumemay be a fully sampled light-field volume; hence, all rays of lightentering the light-field volume 200 may have been captured. Hence, anyvirtual viewpoint within the light-field volume 200, facing anydirection, may be generated.

In FIG. 3, two subviews 300 are generated based on two viewpoints. Thesesubviews 300 may be presented to a viewer of a VR system that shows thesubject matter captured in the light-field volume 200. One subview 300may be generated for each of the viewer's eyes. The ability toaccurately generate subviews may be limited by the sampling patterns,acceptance angles, and surface coverage of the capture system.

Referring to FIG. 9, a capture system 900 is shown, according to oneembodiment. The capture system 900 may contain a set of light-fieldcameras 910 that form a continuous or nearly continuous capture surface920. The light-field cameras 910 may cooperate to fully or partiallycapture a light-field volume, such as the light-field volume 200 of FIG.2.

For each of the light-field cameras 910, there is attached control andreadout circuitry 930. This control and readout circuitry 930 maycontrol the operation of the attached light-field camera 910, and canread captured image and/or video data from the light-field camera 910.

The capture system 900 may also have a user interface 940 forcontrolling the entire array. The user interface 940 may be physicallyattached to the remainder of the capture system 900 and/or may beremotely connected to the remainder of the capture system 900. The userinterface 940 may include a graphical user interface, displays, digitalcontrols, analog controls, and/or any other controls or feedback devicesby which a user can provide input to control the operation of thecapture system 900.

The capture system 900 may also have a primary controller 950 thatcommunicates with and controls all the light-field cameras 910. Theprimary controller 950 may act to synchronize the light-field cameras910 and/or control the individual light-field cameras 910 in asystematic manner.

The capture system 900 may also include data storage 960, which mayinclude onboard and/or remote components for recording the capturedvideo and/or image data generated by the light-field cameras 910. Thedata storage 960 may be physically part of the capture system 900 (forexample, in hard drives, flash memory and/or RAM), removable storage(for example, arrays of SD cards and/or other removable flash storage),and/or remotely connected storage (for example, RAID storage connectedwirelessly or via a wired connection).

The capture system 900 may also include data processing circuitry 970,which may process the image and/or video data as part of the capturesystem 900. The data processing circuitry 970 may include any type ofprocessing circuitry, including but not limited to one or moremicroprocessors, ASICs, FPGA's, and/or the like. In alternativeembodiments, the capture system 900 may simply collect and store rawdata, which may be processed by a separate device such as a computingdevice with microprocessors and/or other data processing circuitry.

In at least one embodiment, the tiled light-field cameras 910 form anoutward-facing ring. One arrangement of a tiled light-field camera array2300 is shown in FIG. 23. In this embodiment, the tiled light-fieldcameras 2310 form a complete 360° ring in a single layer. Light-fieldcameras 2310 that neighbor each other may have overlappingfields-of-view, as shown in the top view on the left. Each of thelight-field cameras 2310 may have a lens surface 2320 that is theoutward-facing surface of a main lens of the light-field camera 2310.Thus, the lens surfaces 2320 may be arranged in a ring pattern.

Another arrangement of a tiled light-field camera array 2400, with 2layers, is shown in FIG. 24. In this embodiment, light-field cameras2410 with lens surfaces 2420 may be arranged in a top layer 2430 thatcaptures a 360° field-of-view that faces partially “up,” and in a bottomlayer 2440 may capture a 360° field-of-view that faces partially “down.”Light-field cameras 2410 that are adjacent to each other within the toplayer 2430 or within the bottom layer 2440 may have overlappingfields-of-view, as shown in the top view on the left. Additionally oralternatively, light-field cameras 2410 of the top layer 2430 may havefields-of-view that overlap those of their adjacent counterparts in thebottom layer 2440, as shown in the side view on the right.

In FIGS. 23 and 24, nine light-field cameras 2310 or light-field cameras2410 are shown in each layer. However, it should be understood that eachlayer may beneficially possess more or fewer light-field cameras 2310 orlight-field cameras 2410, depending on the field-of-view applicable toeach light-field camera. In addition, many other camera arrangements maybe used, which may include additional numbers of layers. In someembodiments, a sufficient number of layers may be used to constitute orapproach a spherical arrangement of light-field cameras.

In at least one embodiment, the tiled light-field cameras are arrangedon the outward facing surface of a sphere or other volume. FIG. 11 showspossible configurations for the tiled array. Specifically, FIG. 11Ashows a tiling pattern 1100 of light-field cameras that creates a cubicvolume. FIG. 11B shows a tiling pattern 1120 wherein quadrilateralregions may be warped in order to approximate the surface of a sphere.FIG. 11C shows a tiling pattern 1140 based on a geodesic dome. In thetiling pattern 1140, the tile shape may alternate between pentagons andhexagons. These tiling patterns are outlined in the darker color. In allof the patterns shown, the number of tiles shown is exemplary, and thesystem may use any number of tiles. In addition, many other volumes andtiling patterns may be constructed.

Notably, the tiles displayed in the tiling pattern 1100, the tilingpattern 1120, and the tiling pattern 1140 represent the maximum extentof the light-field capturing surface for a single light-field camera inthe tiled array. In some embodiments, the physical capture surface mayclosely match the tile size. In other embodiments, the physical capturesurface may be substantially smaller than the tile size.

Size and Field-of-View of the Tiled Array

For many virtual reality and/or augmented reality viewing experiences,“human natural” viewing parameters are desired. In this context, “humannatural” viewing parameters refer specifically to providingapproximately human fields-of-view and inter-ocular distances (spacingbetween the eyes). Further, it is desirable that accurate image and/orvideo data can be generated for any viewpoint as the viewer moves his orher head.

The physical size of the capture surface of the tiled array may bedetermined by the output requirements and fields-of-view of theobjective lenses in the capture system. FIG. 4 conceptually shows therelationship between a physical capture surface, or capture surface 400,with an acceptance or capture surface field-of-view 410 and a virtualfully sampled light-field volume 420. A fully sampled light-field volumeis a volume where all incoming rays from all directions have beencaptured. Within this volume (for example, the sampled light-fieldvolume 420), any virtual viewpoint may be generated, looking anydirection, with any field-of-view.

In one embodiment, the tiled array is of sufficient size and captures asufficient field-of-view to enable generation of viewpoints that allowVR viewers to freely move their heads within a normal range of neckmotion. This motion may include tilting, rotating, and/or translationalmotion of the head. As an example, the desired radius of such a volumemay be 100 mm.

In addition, the field-of-view of the capture surface may be determinedby other desired optical properties of the capture system (discussedlater). As an example, the capture surface may be tiled with lensesarranged in a double Gauss or other known lens arrangement. Each lensmay have an approximately 20° field-of-view half angle.

Referring now to FIG. 34, it can be seen that the physical radius of thecapture surface 400, r_surface, and the capture surface field-of-viewhalf angle, surface_half_fov, may be related to the virtual radius ofthe fully sampled light-field volume, r_complete, by:r_complete=r_surface*sin(surface_half_fov)

To complete the example, in at least one embodiment, the physicalcapture surface, or capture surface 400, may be designed to be at least300 mm in radius in order to accommodate the system design parameters.

In another embodiment, the capture system is of sufficient size to allowusers a nearly full range of motion while maintaining a sittingposition. As an example, the desired radius of the fully sampledlight-field volume 420 may be 500 mm. If the selected lens has a 45°field-of-view half angle, the capture surface 400 may be designed to beat least 700 mm in radius.

In one embodiment, the tiled array of light-field cameras is ofsufficient size and captures sufficient field-of-view to allow viewersto look in any direction, without any consideration for translationalmotion. In that case, the diameter of the fully sampled light-fieldvolume 420 may be just large enough to generate virtual views withseparations large enough to accommodate normal human viewing. In oneembodiment, the diameter of the fully sampled light-field volume 420 is60 mm, providing a radius of 30 mm. In that case, using the lenseslisted in the example above, the radius of the capture surface 400 maybe at least 90 mm.

In other embodiments, a different limited set of freedoms may beprovided to VR viewers. For example, rotation and tilt with stereoviewing may be supported, but not translational motion. In such anembodiment, it may be desirable for the radius of the capture surface toapproximately match the radius of the arc traveled by an eye as a viewerturns his or her head. In addition, it may be desirable for thefield-of-view on the surface of the capture system to match thefield-of-view presented to each eye in the VR headset. In oneembodiment, the radius of the capture surface 400 is between 75 mm and150 mm, and the field-of-view on the surface is between 90° and 120°.This embodiment may be implemented using a tiled array of light-fieldcameras in which each objective lens in the objective lens array is awide-angle lens.

Tiled Array of Plenoptic Light-Field Cameras

Many different types of cameras may be used as part of a tiled array ofcameras, as described herein. In at least one embodiment, thelight-field cameras in the tiled array are plenoptic light-fieldcameras.

Referring to FIG. 1, a plenoptic light-field camera 100 may capture alight-field using an objective lens 110, plenoptic microlens array 120,and photosensor 130. The objective lens 110 may be positioned to receivelight through an aperture (not shown). Each microlens in the plenopticmicrolens array 120 may create an image of the aperture on the surfaceof the photosensor 130. By capturing data regarding the vector at whichlight rays are received by the photosensor 130, the plenopticlight-field camera 100 may facilitate the generation of viewpointswithin a sampled light-field volume that are not aligned with any of thecamera lenses of the capture system. This will be explained in greaterdetail below.

In order to generate physically accurate virtual views from any locationon a physical capture surface such as the capture surface 400 of FIG. 4,the light-field may be captured from as much of the capture surface 400of the capture system as possible. FIGS. 25A and 25B show therelationship between a plenoptic light-field camera such as theplenoptic light-field camera 100 of FIG. 1, and a virtual camera array2500 that are approximately optically equivalent.

In FIG. 25A, the objective lens 110 captures light from within anangular field-of-view 2510. The objective lens 110 has an entrancepupil, the optical image of the aperture stop seen through the front ofthe objective lens 110. The light captured by the objective lens 110passes through the plenoptic microlens array 120, where each microlens2520 in the array creates an N×N pixel “disk image” on the surface ofthe photosensor 130. The disk image is an image of the aperture as seenby the microlens 2520 through which the disk image was received.

The plenoptic light-field camera 100 is approximately opticallyequivalent to a virtual camera array of N×N cameras 2530 with the sameangular field-of-view 2510, with the vertex of each camera 2530 locatedon the surface of the entrance pupil. The size of each entrance pupil inthe virtual camera array 2500 is approximately 1/Nth the size (in onedimension) of the entrance pupil of the objective lens 110. Notably, theterm approximately is used in the description above, as opticalaberrations and other systemic variations may result in deviations fromthe ideal virtual system described.

In order to come as close as possible to a continuous light-fieldcapture surface when spanning multiple cameras, the entrance pupil fromone light-field camera may come as near as possible to adjoining theentrance pupil(s) from neighboring camera(s). FIG. 10 shows a tiledarray 1000 in a ring configuration where the entrance pupils 1010 fromthe objective lenses 1020 create a gap-free surface on the tiled array1000.

In order for the entrance pupils 1010 from neighboring objective lenses1020 to create a nearly continuous surface, the entrance pupil 1010 maybe large relative to the physical size of each light-field camera 1030in the tiled array 1000, as shown in FIG. 10. Further, in order toprovide large viewing angles in as large a volume as possible, it may bebeneficial to start with a lens that has a relatively widefield-of-view. Thus, a good lens design choice may include a relativelywide field-of-view paired with a relatively large aperture (as aperturesize and entrance pupil size are very closely related).

FIG. 13 is a diagram 1300 depicting typical fields-of-view and apertureranges for different types of lens designs. In one embodiment, a doubleGauss lens design 1310 with a low F-number is used for the objectivelens. In alternative embodiments, different lens types may be used,including any of those illustrated on the diagram 1300.

FIG. 14 shows a cross section view of a double Gauss lens design 1400with a large aperture. Double Gauss lenses have a desirable combinationof field-of-view and a potentially large entrance pupil. As an example,50 mm lenses (for 35 mm cameras) are available at F/1.0 and below. Theselenses may use an aperture stop that is greater than or equal to 50 mmon a sensor that is approximately 35 mm wide.

In one embodiment, a tiled array may have plenoptic light-field camerasin which the entrance pupil and aperture stop are rectangular and theentrance pupils of the objective lenses create a continuous or nearlycontinuous surface on the capture system. The aperture stop may beshaped to allow for gap-free tessellation. For example, with referenceto FIG. 10, the entrance pupil 1010 may have a square or rectangularshape. Additionally, one or more lens elements may be cut (for example,squared) to allow for close bonding and to match the shape of theaperture stop. As a further optimization, the layout and packing of themicrolens array, such as the plenoptic microlens array 120 of FIG. 1,may be optimized for the shape of the entrance pupil 1010. For example,the plenoptic microlens array 120 may have a square or rectangular shapeand packing to match a square or rectangular shape of the entrance pupil1010.

In one embodiment, a lens with a relatively wide field-of-view andrelatively large entrance pupil is selected as the objective lens, andthe lenses are spaced as closely as possible while maintaining thetraditional round shape. Again, a double Gauss type lens with a largeaperture may be a good choice for the objective lens.

A tiled array 1500 in a ring configuration using round lenses is shownin FIG. 15. The objective lenses 1520 may be circular, along with theentrance pupils 1510 of the light-field cameras 1530. Thus, the entrancepupils 1510 may not be continuous to each other, as shown in the sideview on the right-hand side. Notably, these types of objective lensesmay be used in any tiling pattern. In another embodiment, thelight-field cameras are arranged into a geodesic dome using twodifferent lens diameters and the tiling pattern 1140 shown in FIG. 11C.Such an arrangement may help to minimize the spacing between theentrance pupils 1510 in order to enhance the continuity of thelight-field data captured.

In one embodiment, one or more top and/or bottom facing cameras may beused in addition to a tiled array in a ring configuration. FIG. 12conceptually depicts a tiled array 1200 with light-field cameras 1210arranged in a ring-shaped pattern, with a single light-field camera 1220facing up. Another light-field camera 1220 (not shown) may be positionedon the opposite side of the tiled array 1200 and may be oriented in adirection opposite to that of the light-field camera 1220.

Notably, the upward and/or downward facing light-field camera(s) 1220may be standard two-dimensional camera(s), light-field camera(s) or acombination thereof. Embodiments of this type may capture highlyincomplete light-field volume data directly above and below the tiledarray 1200, but may offer significant savings in total system costand/or complexity. In some circumstances, the views directly above andbelow the tiled array 1200 may be considered less important than otherdirections. For example, a viewer may not require as much detail and/oraccuracy when looking up or down as when viewing images at his or herelevation.

Changing Rotational Position of the Tiled Array

In at least one embodiment, the surface of a capture system may be madeto change its rotational position and capture different sets ofviewpoints at different times. By changing the rotational positionbetween frames, each successive frame may be used to capture portions ofthe light-field volume that may not have been captured in the previousframe.

Referring to FIGS. 16A through 16C, a sensor array 1600 may be asparsely populated ring of plenoptic light-field cameras 1610. Eachsuccessive frame may capture a different set of angles than the previousframe.

Specifically, at time A, a portion of the light-field volume iscaptured. The sensor array 1600 is then rotated to the position shown attime B by rotating the ring, and another portion of the light-fieldvolume is captured. The sensor array 1600 is rotated again, by onceagain rotating the ring, with another capture at time C.

This embodiment may allow for finer sampling of the light-field volume,more complete sampling of the light-field volume, and/or sampling withless physical hardware. For clarity, the embodiments with changingrotational position are displayed in a ring configuration. However, itshould be recognized that the principle may be applied to any tiledconfiguration. Rotation may be carried out about one axis, as in FIGS.16A through 16C, or multiple axes, if desired. A spherically tiledconfiguration may, for example, be rotated about all three orthogonalaxes.

In one embodiment, the camera array rotates in the same directionbetween each capture, as in FIGS. 16A through 16C. In anotherembodiment, the camera array oscillates between two or more capturepositions and may change the direction of rotation between captures.

For video capture, the overall frame rate of the system may be very highso that every rotational position is captured at a sufficient framerate. As an example, if output video at 60 frames per second is desired,and the capture system uses three distinct and repeating capturepositions, the overall frame capture rate, including time for positionschanges, may be greater than or equal to 180 frames per second. This mayenable samples to be taken at each rotational position insynchronization with the desired frame rate.

In at least one embodiment, the entire sensor array 1600 may be attachedto a rotary joint, which allows the tiled array to rotate independentlyof the rest of the system and surroundings. The electrical connectionsmay go through a slip ring, or rotary electrical interface, to connectrotating components in the system to non-rotating components. Therotation and/or oscillation may be driven by a motor 1620, which may bea stepper motor, DC motor, or any other suitable motor system.

Changing Rotational Position of the Light-Field Sensors

In at least one embodiment, the light-field sensors within the capturesystem may be rotated to capture different sets of viewpoints atdifferent times, while the objective lenses may stay in a fixedposition. By changing the rotational position of the sensors betweenframes, each successive frame may be used to capture portions of thelight-field volume that were not captured in the previous frame.

Referring to FIGS. 17A through 17C, a sensor array 1700 may include aring with a full set of objective lenses 1710 with a sparse set oflight-field sensors 1720. At each time of capture, the sensor array 1700may capture images from a subset of the objective lenses 1710. Theobjective lenses 1710 may maintain a fixed position while the array oflight-field sensors 1720 may rotate.

At time A, a portion of the light-field volume is captured thatcorresponds to the objective lenses 1710 that are actively used at thattime (i.e., the objective lenses 1710 that are in alignment with one ofthe light-field sensors 1720). The light-field sensors 1720 are thenrotated to the position shown at time B, and another portion of thelight-field volume is captured, this time corresponding with thedifferent set of objective lenses 1710 that are in alignment with thelight-field sensors 1720. The light-field sensors 1720 are rotatedagain, with another capture at time C.

This embodiment may allow for finer sampling of the light-field volume,more complete sampling of the light-field volume, and/or sampling withless physical hardware. For clarity, the embodiments with changingrotational position are displayed in a ring configuration. However, itshould be recognized that the principle may be applied to any tiledconfiguration. Rotation may be carried out about one axis, as in FIGS.17A through 17C, or multiple axes, if desired. A spherically tiledconfiguration may, for example, be rotated about all three orthogonalaxes.

In one embodiment, the light-field sensor array rotates in the samedirection between each capture, as in FIGS. 17A through 17C. In anotherembodiment, the light-field sensor array may oscillate between two ormore capture positions and may change the direction of rotation betweencaptures, as in FIGS. 18A through 18C.

FIGS. 18A through 18C depict a sensor array 1800 that may include a ringwith a full set of objective lenses 1810 with a sparse set oflight-field sensors 1820, as in FIGS. 17A through 17C. Again, theobjective lenses 1810 may maintain a fixed position while the array oflight-field sensors 1820 rotates. However, rather than rotating in onecontinuous direction, the array of light-field sensors 1820 may rotateclockwise from FIG. 18A to FIG. 18B, and then counterclockwise from FIG.18B to FIG. 18C, returning in FIG. 18C to the relative orientation ofFIG. 18A. The array of light-field sensors 1820 may thus oscillatebetween two or more relative positions.

In at least one embodiment, the array of light-field sensors 1720 and/orthe array of light-field sensors 1820 may be attached to a rotary joint,which allows the array of light-field sensors 1720 or the array of tiledlight-field sensors 1820 to rotate independently of the rest of thecapture system and surroundings. The electrical connections may gothrough a slip ring, or rotary electrical interface, to connect rotatingcomponents in the system to non-rotating components. The rotation and/oroscillation may be driven by a stepper motor, DC motor, or any othersuitable motor system.

Tiled Array of Array Light-Field Cameras

A wide variety of cameras may be used in a tiled array according to thepresent disclosure. In at least one embodiment, the light-field camerasin the tiled array are array light-field cameras. One example is shownin FIG. 6.

FIG. 6 shows the basic configuration of an array light-field camera 600according to one embodiment. The array light-field camera 600 mayinclude a photosensor 610 and an array of M×N objective lenses 620. Eachobjective lens 620 in the array may focus light onto the surface of thephotosensor 610 and may have an angular field-of-view approximatelyequivalent to the other objective lenses 620 in the array of objectivelenses 620. The fields-of-view of the objective lenses 620 may overlapas shown.

The objective lenses 620 may cooperate to capture M×N virtualviewpoints, with each virtual viewpoint corresponding to one of theobjective lenses 620 in the array. Each viewpoint may be captured as aseparate image. As each objective lens 620 is located at a slightlydifferent position than the other objective lenses 620 in the array,each objective lens 620 may capture approximately the same image, butfrom a different point of view from those of the other objective lenses620. Many variations of the basic design are possible, and any variationmay be applied to the embodiments described below.

FIG. 19 conceptually shows how array light-field cameras 600 as in FIG.6 may be tiled to form a nearly continuous capture surface 1900.Notably, while a ring tiling pattern is displayed in the FIG. 19, anytiling scheme may be used, including but not limited to those of FIGS.11A, 11B, and 11C.

In one embodiment, the resolution and field-of-view of each capturedsubview is approximately equivalent to the desired field-of-view andresolution for later viewing. For example, if the content captured isdesired to be displayed on VR headsets with resolution up to 1920×1080pixels per eye and an angular field-of-view of 90°, each subview maycapture image and/or video data using a lens with a field-of-viewgreater than or equal to 90° and may have a resolution greater than orequal to 1920×1080.

Changing Rotational Position of a Tiled Array of Array Light-FieldCameras

Array light-field cameras and/or components thereof may be rotated toprovide more complete capture of a light-field than would be possiblewith stationary components. The systems and methods of FIGS. 16A through16C, 17A through 17C, and/or 18A through 18C may be applied to arraylight-field cameras like the array light-field camera 600 of FIG. 6.This will be described in greater detail in connection with FIGS. 32Athrough 32C and FIGS. 10A through 10C.

In at least one embodiment, the surface of a capture system having arraylight-field cameras may be made to change its rotational position andcapture different sets of viewpoints at different times. By changing therotational position between frames, each successive frame may be used tocapture portions of the light-field volume that may not have beencaptured in the previous frame, as in FIGS. 16A through 16C.

Referring to FIGS. 32A through 32C, a sensor array 3200 may be asparsely populated ring of array light-field cameras 3210. Eachsuccessive frame may capture a different set of angles than the previousframe.

Specifically, at time A, a portion of the light-field volume iscaptured. The sensor array 3200 is then rotated to the position shown attime B by rotating the ring, and another portion of the light-fieldvolume is captured. The sensor array 3200 is rotated again, by onceagain rotating the ring, with another capture at time C.

This embodiment may allow for finer sampling of the light-field volume,more complete sampling of the light-field volume, and/or sampling withless physical hardware. Further, the benefits of the use arraylight-field cameras may be obtained. For clarity, the embodiments withchanging rotational position are displayed in a ring configuration.However, it should be recognized that the principle may be applied toany tiled configuration. Rotation may be carried out about one axis, asin FIGS. 32A through 32C, or multiple axes, if desired. A sphericallytiled configuration may, for example, be rotated about all threeorthogonal axes.

In one embodiment, the array light-field camera array rotates in thesame direction between each capture, as in FIGS. 32A through 32C. Inanother embodiment, the array light-field camera array oscillatesbetween two or more capture positions and may change the direction ofrotation between captures.

For video capture, the overall frame rate of the system may be very highso that every rotational position is captured at a sufficient framerate. As an example, if output video at 60 frames per second is desired,and the capture system uses three distinct and repeating capturepositions, the overall frame capture rate, including time for positionschanges, may be greater than or equal to 180 frames per second. This mayenable samples to be taken at each rotational position insynchronization with the desired frame rate.

In at least one embodiment, the entire sensor array 3200 may be attachedto a rotary joint, which allows the tiled array to rotate independentlyof the rest of the system and surroundings. The electrical connectionsmay go through a slip ring, or rotary electrical interface, to connectrotating components in the system to non-rotating components. Therotation and/or oscillation may be driven by a stepper motor, DC motor,or any other suitable motor system.

Changing Rotational Position of the Photosensors of Array Light-FieldCameras

In at least one embodiment, the light-field sensors of array light-fieldcameras within the capture system may be rotated to capture differentsets of viewpoints at different times, while the arrays of objectivelenses may stay in a fixed position. By changing the rotational positionof the sensors between frames, each successive frame may be used tocapture portions of the light-field volume that were not captured in theprevious frame.

Referring to FIGS. 20A and 20B, a sensor array 2000 may include a ringwith a full set of arrays of objective lenses 2010 with a sparse set oflight-field sensors 2020. At each time of capture, the sensor array 2000may capture images from a subset of the arrays of objective lenses 2010.The arrays of objective lenses 2010 may maintain a fixed position whilethe array of light-field sensors 2020 may rotate.

At time A, a portion of the light-field volume is captured thatcorresponds to the arrays of objective lenses 2010 that are activelyused at that time (i.e., the arrays of objective lenses 2010 that are inalignment with one of the light-field sensors 2020). The light-fieldsensors 2020 are then rotated to the position shown at time B, andanother portion of the light-field volume is captured, this timecorresponding with the different set of arrays of objective lenses 2010that are in alignment with the light-field sensors 2020. The light-fieldsensors 2020 are rotated again to once again reach the position shown atTime A, and capture may continue to oscillate between the configurationat Time A and that at time B. This may be accomplished via continuous,unidirectional rotation (as in FIGS. 17A through 17C) or via oscillatingmotion in which rotation reverses direction between captures, as inFIGS. 18A through 18C.

This embodiment may allow for finer sampling of the light-field volume,more complete sampling of the light-field volume, and/or sampling withless physical hardware. Further, the benefits of the use arraylight-field cameras may be obtained. For clarity, the embodiments withchanging rotational position are displayed in a ring configuration.However, it should be recognized that the principle may be applied toany tiled configuration. Rotation may be carried out about one axis, asin FIGS. 20A and 20B, or multiple axes, if desired. A spherically tiledconfiguration may, for example, be rotated about all three orthogonalaxes.

In at least one embodiment, the array of light-field sensors 2020 may beattached to a rotary joint, which allows the array of light-fieldsensors 2020 to rotate independently of the rest of the capture systemand surroundings. The electrical connections may go through a slip ring,or rotary electrical interface, to connect rotating components in thesystem to non-rotating components. The rotation and/or oscillation maybe driven by a stepper motor, DC motor, or any other suitable motorsystem.

Using Fiber Optic Tapers to Reduce Gaps in Coverage

In practice, it may be difficult to tile photosensors very close to oneanother. FIG. 33 shows an exemplary CMOS photosensor 3300 in a ceramicpackage 3310. In addition to the active area 3320 on the photosensor3300, there may be space required for inactive die surface, wirebonding, sensor housing, electronic and readout circuitry, and/oradditional components. All space that is not active area is part of thepackage 3310 will not record photons. As a result, when there are gapsin the tiling, there may be missing information in the capturedlight-field volume.

In one embodiment, tapered fiber optic bundles may be used to magnifythe active surface of a photosensor such as the photosensor 3300 of FIG.33. This concept is described in detail in U.S. Provisional ApplicationSer. No. 62/148,055 for “Light Guided Image Plane Tiled Arrays withDense Fiber Optic Bundles for Light-Field and High Resolution ImageAcquisition”, filed Apr. 15, 2015, the disclosure of which isincorporated herein by reference in its entirety.

A schematic illustration is shown in FIG. 21, illustrating an arraylight-field camera 2100. The objective lens array 2120 focuses light onthe large end 2140 of a tapered fiber optic bundle 2130. The taperedfiber optic bundle 2130 transmits the images to the photosensor 2110 anddecreases the size of the images at the same time, as the images movefrom the large end 2140 of the tapered fiber optic bundle 2130 to thesmall end 2150 of the tapered fiber optic bundle 2130. By increasing theeffective active surface area of the photosensor 2110, gaps in coveragebetween array light-field cameras 2100 in a tiled array of the arraylight-field cameras 2100 may be reduced. Practically, tapered fiberoptic bundles with magnification ratios of approximately 3:1 may beeasily acquired.

FIG. 22 conceptually shows how array light-field cameras using fiberoptic tapers, such as the array light-field camera 2100 of FIG. 21, maybe tiled to form a tiled array 2200 in a ring configuration. Usage ofthe tapered fiber optic bundles 2130 may increase the amount ofavailable space between the photosensors 2110, allowing room that may berequired for other purposes.

Array light-field cameras using tapered fiber optic bundles may be usedto create capture surfaces that may otherwise be extremely impractical.Photosensors are generally rectangular, and customization to specificshapes and/or sizes can be extremely time and cost-intensive. Inaddition, tiling options using rectangles can be limited, especiallywhen a goal is to minimize gaps in coverage. In one embodiment, thelarge ends of the tapered fiber optic bundles used in the tiled arrayare cut into a mix of precisely sized and shaped hexagons and pentagons.These tapered fiber optic bundles may then be attached to photosensorsand tiled into a geodesic dome as shown in FIG. 11C. Objective lensesmay be packed onto the geodesic surface as efficiently as possible. Inthis embodiment, each photosensor may capture image and/or video data inregions directly connected to fiber optic bundles that reach the surfaceof the dome (for example, resulting in pentagonal and hexagonal activeareas on the photosensors). See also, the above-referenced U.S.Provisional Application No. 62/148,055 for “Light Guided Image PlaneTiled Arrays with Dense Fiber Optic Bundles for Light-Field and HighResolution Image Acquisition”, filed Apr. 15, 2015, the disclosure ofwhich is incorporated herein by reference.

Focus, Resolution and Aperture Size

Ultimately, the resolution and maximum depth-of-field of virtual viewsgenerated from light-field volume data may be limited to the resolutionand depth-of-field of the captured subviews. In typical practice,subviews in the light-field camera systems described herein have a largedepth-of-field. However, as each subview captures light through anaperture with a physical size, the depth-of-field and of the subview isat least partially determined by the focus of the lens system and thesize of the aperture. Additionally, the resolution of each subview islimited by the resolution of the photosensor pixels used when capturingthat subview as well as the achievable resolution given the optics ofthe system. It may be desirable to maximize both the depth-of-field andthe resolution of the subviews. In practice, the resolution anddepth-of-field of the subviews may need to be balanced against thelimitations of the sensor, the limitations of the available optics, thedesirability of maximizing the continuity of the capture surface, and/orthe desired number of physical subviews.

In at least one embodiment, the focus of the objective lenses in thecapture system may be set to the hyperfocal position of the subviewsgiven the optical system and sensor resolution. This may allow for thecreation of virtual views that have sharp focus from a near distance tooptical infinity.

In one embodiment of an array light-field camera, the aperture of eachobjective lens in the objective lens array may be reduced to increasethe depth-of-field of the subviews. In one embodiment, the aperture sizemay be set so that a desired close focus distance is achievable when theobjective lenses have focus set to their respective hyperfocaldistances.

Virtual View Generation from the Captured Light-Field Data

Once image and/or video data has been captured by the tiled array oflight-field cameras, images for different virtual viewpoints may begenerated. In some embodiments, two images may be generated: one foreach eye. The images may be generated from viewpoints that are displacedfrom each other by the ordinary displacement that exists between twohuman eyes. This may enable the images to present the viewer with theimpression of depth. Image generation may be continuous, and may occurat any frame rate, such as, for example, 24 frames per second (FPS), 30FPS, or 60 FPS, so that the images, in sequence, define a video feed foreach eye. The video feed may be generated in real time as the viewermoves his or her head. Accelerometers, position sensors, and/or othersensors may be used to detect the motion and/or position of the viewer'shead; the resulting position data may be used to move the viewpointsused to generate the images in general synchronization with the viewer'smovements to present the impression of immersion in the capturedenvironment.

Coordinate Conversion from Capture to Light-Field Volume

In at least one embodiment, all pixels in all the light-field cameras inthe tiled array may be mapped to light-field volume coordinates. Thismapping may facilitate the generation of images for different viewpointswithin the light-field volume.

Light-field volume coordinates are shown conceptually in FIG. 5.Light-field volume coordinates are an extended version of standardlight-field coordinates that may be used for panoramic and/oromnidirectional viewing, and may be expressed in terms of rho1, theta1,rho2, theta2. These variables may define a coordinate system 500 that isbased on the polar coordinates of the intersection of a ray with thesurface of two concentric spheres. The inner sphere 510 may have aradius r1 that is large enough to intersect with all rays of interest.Any virtual sphere that fully contains the physical capture system maybe sufficient. The outer sphere 520 may be larger than the inner sphere510. While the outer sphere 520 may be of any size larger that the innersphere 510, it may be conceptually simplest to make the outer sphere 520extremely large (r2 approaches infinity) so that rho2 and theta2 mayoften simply be treated as directional information directly.

This coordinate system 500 may be relative to the entire tiledlight-field capture system. A ray 530 intersects the inner sphere 510 at(rho1, theta1) and the outer sphere 520 at (rho2, theta2). This ray 530is considered to have the 4D coordinate (rho1, theta1, rho2, theta2).

Notably, any coordinate system may be used as long as the location anddirection of all rays of interest can be assigned valid coordinates. Thecoordinate system 500 of FIG. 5 represents only one of many coordinatesystems that may be used to describe the rays of light in a light-fieldvolume in a manner that is global to the light-field camera array. Inalternative embodiments, any other known coordinate system may be used,including but not limited to Cartesian and cylindrical coordinatesystems.

The coordinate system 500 for a light-field volume may be considered toexist in a 3-dimensional Cartesian space, and the origin of thecoordinate system 500 may be located at the center of the inner sphere510 and the outer sphere 520. Coordinates may be converted fromlight-field volume coordinates to Cartesian coordinates by additionallytaking into account the radii of the inner sphere 510 and the outersphere 520. Notably, many rays that may be defined in Cartesiancoordinates may not be able to be represented in the coordinate system500, including all rays that do not intersect the inner sphere 510.

Conceptually, a mapping from a pixel position, indexed in a 2D array byx and y, on a camera, camera, to a light-field volume coordinate in thecoordinate system 500 is a mapping function:f(camera,x,y)->(rho1,theta1, rho2,theta2)

In practice, each pixel, microlens, and subaperture may have a physicalsize; as a result, each pixel may integrate light not from a single ray,but rather a “ray bundle” consisting of a narrow volume of rays. Forclarity, the simplified one-pixel-to-one-ray relationship describedabove will be used herein. However, one skilled in the art willrecognize that this mapping may be naturally extended to cover “raybundles.”

In one embodiment, the mapping function may be determined by the designof the capture system. Using a ray tracer or other optical software, amapping from pixel coordinates to camera-centric world coordinates maybe created. In one embodiment, the ray tracer traces a single,representative ray, from the center of each pixel, through the opticalsystem, and out into the world. That representative ray may beparameterized by its intersection with the entrance pupil and directionof travel.

In another embodiment, many rays may be traced for each pixel,intersecting with the pixel in many locations and from many directions.The rays that are successfully traced from the pixel and out through theobjective lens may be aggregated in some manner (for example, byaveraging or fitting a ray using least squares error regression), and arepresentative ray may be generated. The camera-centric worldcoordinates may then be transformed based on the camera's locationwithin the tiled array, into world coordinates that are consistent toall cameras in the array. Finally, each transformed ray in theconsistent world coordinate space may be traced and intersectionscalculated for the inner and outer spheres that define the light-fieldvolume coordinates.

In one embodiment, a calibration process may determine the mappingfunction after the camera is constructed. The calibration process may beused to fine-tune a previously calculated mapping function, or it may beused to fully define the mapping function.

FIG. 26 shows a diagram 2600 with a set of two charts that may be usedto calibrate the mapping function. More specifically, the diagram 2600includes a cylindrical inner calibration chart, or chart 2610, and acylindrical outer calibration chart, or chart 2620. The chart 2610 andthe chart 2620 are concentric and axially aligned with the capturesystem 2630. Each of the chart 2610 and the chart 2620 contains apattern so that locations on images may be precisely calculated. Forexample, the pattern may be a grid or checkerboard pattern with periodicfeatures that allow for global alignment.

In at least one embodiment, the capture system 2630 may be calibrated asfollows:

-   -   Capture image data with the inner chart 2610 in place    -   For each camera in the array of the capture system 2630:        -   For each subview:            -   Find and register the subview with the global alignment                features            -   For each pixel:                -   Calculate the intersection with the chart as (chi1,                    y1)    -   Remove the inner chart 2610    -   Capture image data with the outer chart 2620 in place        -   For each subview:            -   Find and register the subview with the global alignment                features            -   For each pixel:                -   Calculate the intersection with the chart as (chi1,                    y2)    -   For each pixel:        -   Trace the ray defined by (chi1, y, chi2, y2) to intersect            with the inner sphere 510 in the coordinate system 500 for            the light-field volume to determine (rho1, theta1).        -   Trace the ray defined by (chi1, y, chi2, y2) to intersect            with the outer sphere 520 in the coordinate system 500 for            the light-field volume to determine (rho2, theta2).

Notably, the size and shapes of the chart 2610 and the chart 2620 may bevaried to include spherical charts, cubic charts, or any other type ofsurface or combination thereof. Different chart types may be morereadily adapted to different coordinate systems.

Virtual View Generation from Light-Field Volume Data

Images for virtual reality viewing may be generated from the light-fieldvolume data. These images will be referred to as “virtual views.” Tocreate a virtual view, a virtual lens, virtual focus position, virtualfield-of-view and virtual sensor may be used.

In at least one embodiment, a virtual lens may be centered at thelocation of the desired virtual viewpoint. The virtual lens may containa virtual aperture that may have any shape or size, and thesecharacteristics may partially determine the depth-of-field and bokeh ofthe virtual view. The virtual focus position and virtual field-of-viewof the lens may jointly define a region that will be visible and “infocus” after reconstruction. Notably, the focus and resolution areultimately limited by the focus and resolution of the capture system, soit is possible to reconstruct an image on a virtual focal plane wherenothing is really in focus. The virtual sensor may have the sameresolution as the desired output resolution for the virtual view.

In one embodiment, a virtual camera system may be used to generate thevirtual view. This embodiment is conceptually shown in FIG. 28, inconnection with a coordinate system 2800 having an inner sphere 2810 andan outer sphere 2820. The virtual camera system may have a virtual lens2830 and a virtual sensor 2840 that can be used to generate the virtualview. The configuration of the virtual lens 2830 may determine a virtualfocal plane 2850 with a virtual field-of-view.

In one embodiment, an ideal lens is assumed, and the virtual setup maybe simplified. This embodiment is conceptually shown in FIG. 29, inconnection with a coordinate system 2900 having an inner sphere 2910 andan outer sphere 2920. In this simplified model, the sensor pixels may bemapped directly onto the surface of the focal plane and more complicatedray tracing may be avoided.

Specifically, the lens may be geometrically simplified to a surface (forexample, a circular disc) to define a virtual lens 2930 inthree-dimensional Cartesian space. The virtual lens 2930 may representthe aperture of the ideal lens. The virtual field-of-view and virtualfocus distance, when taken together, define an “in focus” surface inthree-dimensional Cartesian space with the same aspect ratio as thevirtual sensor. A virtual sensor 2940 may be mapped to the “in focus”surface.

The following example assumes a set of captured rays parameterized inlight-field volume coordinates, rays, a circular virtual aperture, va, arectangular virtual sensor with width w and height h, and rectangular“in focus” surface, fs. An algorithm to create the virtual view may thenbe the following:

-   -   view_image=new Image(w, h)    -   view_image.clear( )    -   for each ray in rays        -   cart_ray=convert_to_cartesian3d(ray)        -   if (intersects(cart_ray, va) && intersects(cart_ray, fs))            -   point=intersection(cart_ray, fs)            -   norm_point=normal ize_point_relative_to(fs)            -   sensor_x=norm_point.x*w            -   sensor_y=norm_point.y*h            -   accumulate(view_image, x, y, ray.color)    -   where:        -   intersects returns true if the supplied ray intersects with            the surface        -   intersection returns the location, in Cartesian coordinates,            of intersection        -   normal ize_point_relative_to normalizes a Cartesian 3D point            into a normalized 2D location on the provided surface.            Values are in x=[0,1] and y=[0,1]        -   accumulate accumulates the color values assigned to the ray            into the image. This method may use any sort of            interpolation, including nearest neighbor, bilinear,            bicubic, or any other method.

In another embodiment, the virtual lens and/or the virtual sensor may befully modeled as a more complete optical system. This embodiment isconceptually shown in FIG. 30, which illustrates modeling in the contextof a coordinate system 3000 having an inner sphere 3010 and an outersphere 3020. The embodiment may consist of a virtual sensor 3040 and avirtual lens 3030, each with size and shape in Cartesian coordinates. Inthis case, rays in the captured light-field volume may be traced throughthe virtual lens 3030 and ultimately intersected (or not) with thevirtual sensor 3040.

The virtual sensor 3040 may consist of virtual optical components,including one or more virtual lenses, virtual reflectors, a virtualaperture stop, and/or additional components or aspects for modeling. Inthis embodiment, rays that intersect with the entrance to the virtuallens 3030 may be optically traced through the virtual lens 3030 and ontothe surface of the virtual sensor 3040.

FIG. 31 shows exemplary output 3100 from an optical ray tracer. In theimage, a set of rays 3110 are refracted through the elements 3120 in alens 3130 and traced to the intersection point 3140 on a virtual sensorsurface 3150.

The following example assumes a set of captured rays parameterized inlight-field volume coordinates, rays, a virtual lens, vl, that containsa virtual entrance pupil, vep, and a rectangular virtual sensor, vs,with width w and height h. An algorithm to create the virtual view maythen be the following:

-   -   view_image=new Image(w, h)    -   view_image.clear( )    -   for each ray in rays        -   cart_ray=convert_to_cartesian3d(ray)        -   if (intersects(cart_ray, vep))            -   image_ray=trace_ray_through_lens(cart_ray, vl)            -   if (intersects(image_ray, vs))                -   point=intersection(cart_ray, vs)                -   norm_point=normal ize_point_relative_to(vs)                -   sensor_x=norm_point.x*w                -   sensor_y=norm_point.y*h                -   accumulate(view_image, x, y, image_ray.color)            -   Where:                -   intersects returns true if the supplied ray                    intersects with the surface                -   intersection returns the location, in Cartesian                    coordinates, of intersection                -   trace_ray_through_lens traces a ray through the                    virtual lens                -   normal ize_point_relative_to normalizes a Cartesian                    3D point into a normalized 2D location on the                    provided surface. Values are in x=[0,1] and y=[0,1]                -   accumulate accumulates the color values assigned to                    the ray into the image. This method may use any sort                    of interpolation, including nearest neighbor,                    bilinear, bicubic, or any other method.

Notably, optical ray tracers (for example, commercial applications suchas ZEMAX) may function with varying levels of complexity as the behaviorof light in the physical world is extremely complex. The above examplesassume that one ray of light from the world equates to a single ray oflight after passing through an optical system. Many optical modelingprograms will model additional complexities such as chromaticdispersion, diffraction, reflections, and absorption.

Synthetic Ray Generation

In some embodiments of the capture system, certain areas of thelight-field volume may not be adequately sampled. For example, FIG. 15shows a tiled array 1500 in the form of a ring arrangement oflight-field cameras 1530 in which gaps exist between the entrance pupilsof the light-field cameras 1530 in the tiled array 1500. Light from theworld that intersects with the tiled array 1500 in the gaps will not berecorded. While the sizes of the gaps in a light-field capture systemmay be extremely small relative to those of prior art systems, thesegaps may still exist in many embodiments. When virtual views aregenerated that require ray data from the inadequately sampled regions ofthe light-field volume, these rays may be synthetically generated.

In one embodiment, rays are synthetically generated using simpleinterpolation between the closest available samples based on theirlight-field volume coordinates. Simple interpolation may work well whenthe difference between the location of the available samples and thedesired sample is small. Notably, small is a relative term, anddependent on many factors, including the resolution of the virtual view,the location of physical subjects in the world at the time of capture,the application's tolerance for errors, and a host of other factors. Thesimple interpolation may generate a new sample value based on a weightedaverage of the neighboring rays. The weighting function may use nearestneighbor interpolation, linear interpolation, cubic interpolation,median filtering or any other approach now known or later developed.

In another embodiment, rays are synthetically generated based on athree-dimensional model and/or a depth map of the world at the time ofcapture. Notably, in a system that is well-calibrated relative to theworld, a depth map and a three-dimensional model may be easilyinterchangeable. For the duration of the description, the term depth mapwill be used. In this embodiment, a depth map may be generatedalgorithmically from the captured light-field volume.

Depth map generation from light-field data and/or multiple overlappingimages is a complicated problem, but there are many existing algorithmsthat attempt to solve the problem. See, for example, the above-citedU.S. patent application Ser. No. 14/302,826 for “Depth Determination forLight Field Images”, filed Jun. 12, 2014 and issued as U.S. Pat. No.8,988,317 on Mar. 24, 2015, the disclosure of which is incorporatedherein by reference.

Once a depth map has been generated, a virtual synthetic ray may betraced until it reaches an intersection with the depth map. In thisembodiment, the closest available samples from the captured light-fieldvolume may be the rays in the light-field that intersect with the depthmap closest to the intersection point of the synthetic ray. In oneembodiment, the value assigned to the synthetic ray may be a new samplevalue based on a weighted average of the neighboring rays. The weightingfunction may use nearest neighbor interpolation, linear interpolation,cubic interpolation, median filtering, and/or any other approach nowknown or later developed.

In another embodiment, a pixel infill algorithm may be used ifinsufficient neighboring rays are found within an acceptable distance.This situation may occur in cases of occlusion. For example, aforeground object may block the view of the background from theperspective of the physical cameras in the capture system. However, thesynthetic ray may intersect with the background object in the occludedregion. As no color information is available at that location on thebackground object, the value for the color of the synthetic ray may beguessed or estimated using an infill algorithm. Any suitable pixelinfill algorithms may be used. One exemplary pixel infill algorithm is“PatchMatch,” with details as described in C. Barnes et al., PatchMatch:A Randomized Correspondence Algorithm for Structural Image Editing ACMTransactions on Graphics (Proc. SIGGRAPH), August 2009.

Virtual View Generation Acceleration Structures

In some cases, the algorithms for virtual view generation cited abovemay not execute efficiently enough, may not execute quickly enough,and/or may require too much data or bandwidth to properly enable viewingapplications. To better enable efficient processing and/or viewing, thecaptured data may be reorganized or resampled as appropriate.

In one embodiment, the captured data may be resampled into a regularizedformat. In one specific embodiment, the light-field is resampled into afour-dimensional table, with separate dimensions for rho1, theta1, rho2and theta2. The size of the resampled table will depend on many factors,including but not limited to the intended output resolution of thevirtual views and the number of discrete viewpoints from which virtualviews may be generated. In one embodiment, the intended linear outputresolution of a virtual view may be 1000 pixels, and the field-of-viewmay be 100°. This may result in a total sampling of 3600 pixels for360°. In the same embodiment, it may be desired that 100 discreteviewpoints can be generated in a single dimension. In this case, thesize of the four-dimensional table may be 100×100×3600×3600. Notably,large sections of the table may be empty of data, and the table may bedramatically compressed relative to its nominal size. The resampled,regularized data structure may be generated through the use of“splatting” algorithms, “gathering” algorithms, or any other algorithmor technique.

In an embodiment using a “splatting” algorithm, the resampling processmay begin with a four-dimensional table initialized with empty values.The values corresponding to each ray in the captured data set may thenbe added into the table at the data index(es) that best match thefour-dimensional coordinates of the ray. The adding may use anyinterpolation algorithm to accumulate the values, including but notlimited to a nearest neighbor algorithm, a quadlinear algorithm, aquadcubic algorithm, and/or combinations or variations thereof.

In an embodiment using a “gathering” algorithm, the value for each dataindex in the 4D table is calculated by interpolating from the nearestrays in the captured light-field data set. In one specific embodiment,the value at each index is a weighted sum of all rays that havecoordinates within a four-dimensional hypercube centered at thecoordinates corresponding to the data index. The weighting function mayuse nearest neighbor interpolation, linear interpolation, cubicinterpolation, median filtering or any other approach now known or laterdeveloped.

After the captured light-field data set has been resampled into thefour-dimensional table, there may be locations in the table with valuesthat remain uninitialized or that have accumulated very little data.These locations may be referred to as “holes”. In some cases, it may bedesirable that the “holes” are filled in prior to the performance ofadditional processing like virtual view generation. In one embodiment,holes may be filled in using four-dimensional interpolation techniquesin which values for the holes are interpolated based on the values oftheir neighbors in the four-dimensional table. The interpolation may useany type of filter kernel function, including but limited to linearfunctions, median filter functions, cubic functions, and/or syncfunctions. The filter kernel may be of any size.

In another embodiment, “hole” data may be filled in using pixel infillalgorithms. In a specific example, to fill hole data for the index withcoordinates (rho1, theta1, rho2, theta2), a two-dimensional slice ofdata may be generated by keeping rho1 and theta1 fixed. A pixel infillalgorithm (for example, PatchMatch) may be applied to fill in themissing data in the two-dimensional slice, and the generated data valuesmay then be added into the four-dimensional table.

In one embodiment, the resampled four-dimensional table may be dividedand stored in pieces. In some embodiments, each piece may correspondwith a file stored in a file system. As an example, the fullfour-dimensional table may be broken up by evenly in four pieces bystoring ¼×¼×¼×¼ of the full table in each piece. One advantage of thistype of approach may be that entire pieces may be completely empty, andmay thus be discarded. Another advantage may be that less informationmay need to be loaded in order to generate a virtual view.

In one embodiment, a set of virtual views is precomputed and stored. Insome embodiments, a sufficient number of virtual views may beprecomputed to enable the display of any needed viewpoint from theprecomputed virtual views. Thus, rather than generating virtual views inreal-time, the viewing software may read and display the precomputedvirtual views. Alternatively, some precomputed virtual views may be usedin combination with real time generation of other virtual views.

Conventional Camera Arrays

In some embodiments, conventional, two-dimensional cameras may be usedin order to provide additional spatial resolution, cost reduction, moremanageable data storage, processing, and/or transmission, and/or otherbenefits. Advantageously, such conventional cameras may be arranged in atiled array similar to those described above for light-field cameras.Such arrays may also be arranged to provide continuous, or nearlycontinuous, fields-of-view.

Referring to FIGS. 35A through 35D, perspective and side elevation viewsdepict a tiled array 3500 of conventional cameras, according to oneembodiment. As shown, the tiled array 3500 may have three differenttypes of cameras, including upper view cameras 3510, center view cameras3520, and lower view cameras 3530. In the tiled array 3500, the upperview cameras 3510, the center view cameras 3520, and the lower viewcameras 3530 may be arranged in an alternating pattern, with a centerview camera 3520 between each upper view camera 3510 and each lower viewcamera 3530. Thus, the tiled array 3500 may have as many of the centerview cameras 3520 as it has of the lower view cameras 3530 and the upperview cameras 3510, combined. The larger number of center view cameras3520 may provide enhanced and/or more complete imaging for the centerview, in which the viewer of a virtual reality experience is likely tospend the majority of his or her viewing time.

As shown in FIGS. 35B and 35D, the upper view cameras 3510 and the lowerview cameras 3530 may each have a relatively large field-of-view 3540,which may be 120° or larger. As shown in FIG. 35C, the center viewcameras 3520 may each have a field-of-view 3550 that approximates thatof the headset the user will be wearing to view the virtual realityexperience. This field-of-view 3550 may be, for example, 90° to 110°.The placement of the upper view cameras 3510 and the lower view cameras3530 may be relatively sparse, by comparison with that of the centerview cameras 3520, as described above.

Referring to FIG. 36, a diagram 3600 depicts stitching that may be usedto provide an extended vertical field-of-view 3610. A 200° or greatervertical field-of-view 3610 may be obtained at any point along the tiledarray 3500 with only “close” stitching. Additional verticalfield-of-view may be constructed with “far” stitching. Advantageously,the tiled array 3500 may have full support for three angular degrees offreedom and stereo viewing. Further, the tiled array 3500 may providelimited support for horizontal parallax and/or limited stitching, exceptfor extreme cases. Alternative embodiments may provide support for headtilt, vertical parallax, and/or forward/backward motion. One embodimentthat provides some of these benefits will be shown and described inconnection with FIG. 37.

Referring to FIG. 37, a perspective view depicts a tiled array 3700according to another alternative embodiment. As shown, the tiled array3700 may have three different types of cameras, including upper viewcameras 3710, center view cameras 3720, and lower view cameras 3730. Asin the previous embodiment, each of the upper view cameras 3710 and thelower view cameras 3730 may have a field-of-view 3740 that is relativelylarge, for example, 120° or larger. Each of the center view cameras 3720may have a field-of-view 3750 that is somewhat smaller, for example, 90°to 110°.

The upper view cameras 3710, the center view cameras 3720, and the lowerview cameras 3730 may be arranged in three rows, including a top row3760, a middle row 3770, and a bottom row 3780. In the top row 3760, theupper view cameras 3710 and the center view cameras 3720 may be arrangedin an alternating pattern. In the middle row 3770, only the center viewcameras 3720 may be present. In the bottom row, 3780, the lower viewcameras 3730 and the center view cameras 3720 may be arranged in analternating pattern similar to that of the upper view cameras 3710 andthe center view cameras 3720 of the top row 3760. The tiled array 3700may have approximately four times as many of the center view cameras3720 as of each of the upper view cameras 3710 and the lower viewcameras 3730. Thus, as in the previous embodiment, more complete imagingmay be provided for the center views, in which the viewer of a virtualreality experience is likely to spend the majority of his or her viewingtime. Notably, the center view cameras 3720 on the top row 3760 may betilted upward, and the center view cameras 3720 on the bottom row 3780may be tilted downward. This tilt may provide enhanced verticalstitching and/or an enhanced vertical field-of-view.

Further, the tiled array 3700 may have three full degrees of freedom,and three limited degrees of freedom. The tiled array 3700 may providesupport for head tilt via the enhanced vertical field-of-view, and mayfurther provide limited vertical parallax. Further, the tiled array 3700may support limited forward/backward movement.

In other alternative embodiments, various alterations may be made inorder to accommodate user needs or budgetary restrictions. For example,fewer cameras may be used; in some tiled array embodiments, only ten totwenty cameras may be present. It may be advantageous to use smallercameras with smaller pixel sizes. This and other modifications may beused to reduce the overall size of the tiled array. More horizontaland/or vertical stitching may be used.

According to one exemplary embodiment, approximately forty cameras maybe used. The cameras may be, for example, Pt Grey Grasshopper 3 machinevision cameras, with CMOSIS MCV3600 sensors, USB 3.0 connectivity, andone-inch, 2 k×2 k square image sensors, with 90 frames per second (FPS)capture and data transfer capability. The data transfer rate for rawimage data may be 14.4 GB/s (60 FPS at 12 bits), and a USB 3.0 to PCIEadapter may be used. Each USB 3.0 interface may receive the image datafor one camera.

The tiled array may have a total resolution of 160 megapixels. Each ofthe center view cameras may have a Kowa 6 mm lens with a 90°field-of-view. Each of the upper view cameras and lower view cameras mayhave a Fujinon 2.7 mm fisheye lens with a field-of-view of 180° or more.In alternative embodiments, more compact lenses may be used to reducethe overall size of the tiled array.

Conventional cameras may be arranged in tiled arrays according to a widevariety of tiled arrays not specifically described herein. With the aidof the present disclosure, a person of skill in the art would recognizethe existence of many variations of the tiled array 3500 of FIG. 35 andthe tiled array 3700 that may provide unique advantages for capturingvirtual reality video streams.

Spatial Random Access Enabled Volumetric Video—Introduction

As described in the background, the capture process for volumetric videomay result in the generation of large quantities of volumetric videodata. The amount of volumetric video data may strain the storage,bandwidth, and/or processing capabilities of client computing systemsand/or networks. Accordingly, in at least one embodiment, the volumetricvideo data may be divided into portions, and only the portion needed, orlikely to be needed soon, by a viewer may be delivered.

Specifically, at any given time, a viewer is only able to observe afield-of-view (FoV) inside the viewing volume. In at least oneembodiment, the system only fetches and renders the needed FoV from thevideo volume data. To address the challenges of data and complexity, aspatial random access coding and viewing scheme may be used to allowarbitrary access to a viewer's desired FoV on a compressed volumetricvideo stream. Inter-vantage and inter spatial-layer predictions may alsobe used to help improve the system's coding efficiency.

Advantages of such a coding and/or viewing scheme may include, but arenot limited to, the following:

-   -   Reduction of the bandwidth requirement for transmission and        playback;    -   Provision of fast decoding performance for responsive playback;        and/or    -   Enablement of low-latency spatial random access for interactive        navigation inside the viewing volume.        Spatial Random Access Enabled Volumetric Video—Encoding

Several different methods may be used to apportion the video data,associate the video data with the corresponding vantage, encode thevideo data, and/or compress the video data for subsequent transmission.Some exemplary methods will be shown and described, as follows. Thesecan be implemented singly or in any suitable combination.

Data Representation—Vantages

Numerous data representations are possible for video data for fullyimmersive virtual reality and/or augmented reality (hereafter “immersivevideo”). “Immersive video” may also be referred to as “volumetric video”where there is a volume of viewpoints from which the views presented tothe user can be generated. In some data representations, digitalsampling of all view-dependent color and depth information may becarried out for any visible surfaces in a given viewing volume. Suchsampling representation may provide sufficient data to render anyarbitrary viewpoints within the viewing space. Viewers may enjoy smoothview-dependent lighting transitions and artifacts-free occlusion fillingwhen switching between different viewpoints.

As described in the above-cited U.S. patent application for “VantageGeneration” U.S. Pat. No. 15/590,841, for ease of spatial random accessand viewport rendering, an image-based rendering system according to thepresent disclosure may represent immersive video data by creating athree-dimensional sampling grid over the viewing volume. Each point ofthe sampling grid is called a “vantage.” Various vantage arrangementsmay be used, such as a rectangular grid, a polar (spherical) matrix, acylindrical matrix, and/or an irregular matrix. Each vantage may containa projected view, such as an omnidirectional view projected onto theinterior of a sphere, of the scene at a given coordinate in the samplinggrid. This projected view may be encoded into video data for thatparticular vantage. It may contain color, texture, and/or depthinformation. Additionally or alternatively, the projected view may becreated using the virtual view generated from the light-field volumedata, as discussed in the previous section.

To provide smooth transitions for view-dependent lighting and rendering,the system may perform a barycentric interpolation of color between fourvantages whose locations form a tetrahedron that includes the viewposition for each eye view. Other fusion techniques may alternatively oradditionally be used to interpolate between vantages. The result may bethe combination of any number of vantages to generate viewpoint videodata for a viewpoint that is not necessarily located at any of thevantages.

Tile-Based Vantage Coding

A positional tracking video experience may require more than hundreds ofhigh resolution omnidirectional vantages across the viewing volume. Thismay require at least two orders of magnitude more storage space, bycomparison with conventional two-dimensional videos. With color anddepth information represented in each of the vantages, image-basedand/or video-based compression techniques, such as JPEG, H.264/AVCand/or HEVC, may be applied to the color and/or depth channels to removeany spatial and temporal redundancies within a single vantage stream, aswell as redundancies between different vantage streams.

In many situations, during decoding and rendering, there may be a needfor multiple vantages to be loaded and rendered in real-time at a highframe rate. A compressed vantage, which requires a decoding procedure,may further put computation and memory pressure on the client's system.To relieve this pressure, in at least one embodiment, the system andmethod may only decode and render the region of vantages within aviewer's FoV. Spatial random access may be facilitated by dividing avantage into multiple tiles. Each tile may be independently and/orjointly encoded with the system's vantage encoder using image-basedand/or video-based compression techniques, or encoded through the use ofany other compression techniques. When a user is accessing an arbitraryviewpoint inside a viewing volume, the system may find the correspondingvantages within the sampling grid and fetch the corresponding tilesinside the vantages. A tile-based representation may also offer inherentparallelizability for multi-core systems. The tiling scheme used forvantage compression may be different from the tiling scheme used forrendering or culling used by the rendering pipeline. Notably, tiling maybe used to expedite delivery, decoding, and/or display of video data,independently of the use of compression. Tiling may expedite playbackand rendering independently of the manner in which tiling is performedfor encoding and/or transmission. In some embodiments, the tiling schemeused for encoding and transmission may also be used be used for playbackand rendering. A tiled rendering scheme may help reduce computationcomplexity and provide stability to meet time-varying demands on the CPUand/or GPU of a computing system.

Referring to FIG. 52, a series of graphs depict a tile-based scheme5200, according to one embodiment. The tile-based scheme 5200 may allowspatial random access on a single vantage for any field of view within a360° field, i.e., any viewing direction originating at the vantage. FIG.52 illustrates fetched tiles, on the bottom row, that correspond tovarious input fields, on the top row, showing a top-down view of theinput field-of-view projected on a single spherical vantage. Each planarimage is projected to a planar image from a single omnidirectionalspherical vantage.

Multiple Resolution Layers

Coding dependencies, system processing, and/or network transmission mayintroduce spatial random access latency to the system. Spatial randomaccess to different tiles may be needed in certain instances, such aswhen the viewer switches the FoV in a virtual reality experience byturning his or her head or when the viewer moves to a new region alongthe vantage sampling grid. To prevent playback discontinuity in suchsituations, the system and method disclosed herein may pre-load thetiles outside a viewer's FoV. However, this may increase the decodingload on the client system and limit the complexity savings provided bythe compression and/or apportionment of the video data. Accordingly, thepre-fetched tiles may instead be provided at a lower spatial resolution,so as to conceal switching latency.

In addition, clients with different constraints, such as networkbandwidth, display resolution and computation capabilities, may requiredifferent quality representation of the tiles. In at least oneembodiment, the system and method provide such different qualityrepresentations by displaying the tiles at different resolutions and/ordelivering the tiles at different bitrates. To meet these demands, amulti-spatial resolution layer scheme may be used. A system according tothe present disclosure may have any number of spatial resolution layers.Further, all tiles need not necessarily have the same number of spatialresolution layers; rather, different tiles may have different numbers ofspatial resolution layers. Different tiles may additionally oralternatively have different bit rates, spatial resolutions, frameresolutions, shapes, and/or aspect ratios. A spatial layered scheme mayalso provide error-resilience against data corruption and network packetlosses.

FIG. 38 illustrates a simplified example of tiles with multiple spatiallayers. A tile 3800 is shown, representing some or all of the viewencoded in the video data for a single vantage, including three layers,according to one embodiment. Specifically, the tile 3800 may have afirst layer 3810, a second layer 3820, and a third layer 3830. The firstlayer 3810 may be a low resolution layer, the second layer 3820 may be amedium resolution layer, and the third layer 3830 may be a highresolution layer.

Thus, the first layer 3810 may be transmitted and used to generate anddisplay the viewpoint video data when bandwidth, storage, and/orcomputational limits are stringent. The second layer 3820 may betransmitted and used to generate and display the viewpoint video datawhen bandwidth, storage, and/or computational limits are moderate. Thethird layer 3830 may be transmitted and used to generate and display theviewpoint video data when bandwidth, storage, and/or computationallimits are less significant.

Tiling Design for Equirectangular Projected Vantage

In some embodiments, equirectangular projection can be used to project agiven scene onto each vantage. In equirectangular projection, apanoramic projection may be formed from a sphere onto a plane. This typeof projection may create non-uniform sampling densities. Due to constantspacing of latitude, this projection may have a constant verticalsampling density on the sphere. However, horizontally, each latitude φ,may be stretched to a unit length to fit in a rectangular projection,resulting in a horizontal sampling density of 1/cos(φ). Therefore, toreduce the incidence of over-sampling in equirectangular projection,there may be a need to scale down the horizontal resolution of each tileaccording to the latitude location of the tile. This re-sampling ratemay enable bit-rate reduction and/or maintain uniform spatial samplingacross tiles.

Referring to FIGS. 53A and 53B, exemplary tiling schemes 5300 and 5350are depicted, according to certain embodiments. The tiling scheme 5300is a uniform equirectangular tiling scheme, and the tiling scheme is anequirectangular tiling scheme with reduced horizontal sampling at thetop and bottom. The formula shown in FIGS. 53A and 53B may be used toreduce the width of some of the tiles of the tiling scheme 5300 of FIG.53A to obtain the reduced horizontal resolution in the tiling scheme5350 of FIG. 53B.

In alternative embodiments, in addition to or instead of re-sampling thedimension of the tile, the length of pixels in scanline order may beresampled. Such resampling may enable the use of a uniform tilingscheme, as in FIG. 53A. As a result, the system can maintain constantsolid angle quality. This scheme may leave some of the tiles near thepoles blank (for example, the tiles at the corners of FIG. 53A); thesetiles may optionally be skipped while encoding. However, under thisscheme, the playback system may need to resample the pixels in scanlineorder for proper playback, which might incur extra system complexities.

Compression Scheme References

In recent years, a number of compression schemes have been developedspecifically for two-dimensional, three-dimensional, and multi-viewvideos. Examples of various compression standards include:

-   -   1. G. Tech, Y. Chen, K. Muller, J.-R. Ohm, A. Vetro, and Y.-K.        Wang, “Overview of the Multiview and 3D Extensions of High        Efficiency Video Coding”, IEEE Transactions on Circuits and        Systems for Video Technology, Vol. 26, Issue 1, pp. 35-49,        September 2015.    -   2. Jens-Rainer Ohm, Mihaela van der Schaar, John W. Woods,        Interframe wavelet coding—motion picture representation for        universal scalability, Signal Processing: Image Communication,        Volume 19, Issue 9, October 2004, Pages 877-908, ISSN 0923-5965.    -   3. Chuo-Ling Chang, Xiaoqing Zhu, P. Ramanathan and B. Girod,        “Light field compression using disparity-compensated lifting and        shape adaptation,” in IEEE Transactions on Image Processing,        vol. 15, no. 4, pp. 793-806, April 2006.    -   4. K. Yamamoto et al., “Multiview Video Coding Using View        Interpolation and Color Correction,” in IEEE Transactions on        Circuits and Systems for Video Technology, vol. 17, no. 11, pp.        1436-1449, November 2007.    -   5. Xiu, Xiaoyu, Derek Pang, and Jie Liang. “Rectification-Based        View Interpolation and Extrapolation for Multiview Video        Coding.” IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO        TECHNOLOGY 21.6 (2011): 693.    -   6. Park, Joon Hong, and Hyun Wook Park. “A mesh-based disparity        representation method for view interpolation and stereo image        compression.” Image Processing, IEEE Transactions on 15.7        (2006): 1751-1762.    -   7. P. Merkle, K. Müller, D. Marpe and T. Wiegand, “Depth Intra        Coding for 3D Video Based on Geometric Primitives,” in IEEE        Transactions on Circuits and Systems for Video Technology, vol.        26, no. 3, pp. 570-582, March 2016. doi:        10.1109/TCSVT.2015.2407791.    -   8. G. J. Sullivan, J. M. Boyce, Y. Chen, J.-R. Ohm, C. A.        Segall, and A. Vetro, “Standardized Extensions of High        Efficiency Video Coding”, IEEE Journal on Selected Topics in        Signal Processing, Vol. 7, no. 6, pp. 1001-1016, December 2013.        http://ieeexploreleee.org/stamp/stamp.jsp?tp=&arnumber=6630053.    -   9. Aditya Mavlankar and Bernd Girod,        “Spatial-Random-Access-Enabled Video Coding for Interactive        Virtual Pan/Tilt/Zoom Functionality,” IEEE Transactions on        Circuits and Systems for Video Technology. vol. 21, no. 5, pp.        577-588, May 2011.    -   10. Mavlankar, P. Agrawal, D. Pang, S. Halawa, N. M. Cheung        and B. Girod, “An interactive region-of-interest video streaming        system for online lecture viewing,” 2010 18th International        Packet Video Workshop, Hong Kong, 2010, pp. 64-71.    -   11. Fraedrich, Roland, Michael Bauer, and Marc Stamminger.        “Sequential Data Compression of Very Large Data in Volume        Rendering.” VMV. 2007.    -   12. Sohn, Bong-Soo, Chandrajit Bajaj, and Vinay Siddavanahalli.        “Feature based volumetric video compression for interactive        playback.” Proceedings of the 2002 IEEE symposium on Volume        visualization and graphics. IEEE Press, 2002.

Any of the foregoing may optionally be incorporated into the systems andmethods disclosed herein. However, most image/video-based compressionschemes exploit redundancies that exist, for example, between differentcamera views (inter-view) and between different video frames in time(inter-frame). Recent standards, such as MV/3D-HEVC, aim to compressvideo-plus-depth format more efficiently by addressing the uniquecharacteristics of depth maps and exploiting redundancies between theviews. Numbers 1, 7, and 8 above are examples of such standards.

The compression schemes set forth above generally rely on block-baseddisparity compensation and expect all input views to be aligned in aone-dimensional linear and coplanar arrangement. Numbers 4 and 5 makeuse of the geometric relationship between different camera views andgenerate a synthesized reference view using view interpolation andextrapolation. Number 6 represents disparity information using meshesand yields higher coding gains with higher quality view interpolationfor stereo image compression. Other prior techniques also utilizelifting-based wavelet decomposition to encode multi-view data byperforming motion-compensated temporal filtering, as in Number 2 above,and disparity-compensated inter-view filtering, as in Number 3 above.However, all inter-view techniques described above have only appliedon-camera data with planar projection. In terms of spatial random accessenabled video, Numbers 9 and 10 provide a rectangular tiling scheme withmulti-spatial resolution layers and enabled pan/tilt/zoom capabilitieson high-resolution two-dimensional videos.

Prediction Types

Referring to FIG. 39, an encoder 3900 is depicted, according to oneembodiment. The encoder 3900 may have a compressor 3910 and adecompressor 3920. The encoder 3900 may employ intra-frame prediction3930, inter-frame prediction 3940, inter-vantage prediction 3950, and/orinter-spatial layer prediction 3960 to compress color information for agiven input. The input can be a single vantage frame, a tile inside avantage, and/or a block inside a tile. The encoder 3900 may utilizeexisting techniques, such as any of the list set forth in the previoussection, in intra-frame coding to remove redundancies through spatialprediction and/or inter-frame coding to remove temporal redundanciesthrough motion compensation and prediction. The encoder 3900 may useprojection transform 3970 and inverse projection transform 3975. Theencoder 3900 may also have a complexity/latency-aware RDO encodercontrol 3980, and may store vantage data in a vantage bank 3990.

By contrast with conventional inter-view prediction in stereo images orvideo, the inter-vantage prediction carried out by the encoder 3900 maydeal with non-planar projection. It may generate meshes by extractingcolor, texture, and/or depth information from each vantage and renderinga vantage prediction after warping and interpolation. Other methods forinter-vantage prediction include, but are not limited to:

-   -   Geometric transformation by using depth information and known        intrinsic and extrinsic camera parameters;    -   Methods mentioned in previous section on virtual view generation        from light field data;    -   Reprojection techniques described in the above-referenced U.S.        patent application for “Vantage Generation” U.S. Pat. No.        15/590,841, and    -   Other advanced view synthesis method, such as those set forth        in:        -   Zitnick, C. Lawrence, et al. “High-quality video view            interpolation using a layered representation.” ACM            Transactions on Graphics (TOG). Vol. 23. No. 3. ACM, 2004;        -   Flynn, John, et al. “DeepStereo: Learning to Predict New            Views from the World's Imagery.” arXiv preprint            arXiv:1506.06825 (2015); and        -   Oh, Kwan-Jung, Sehoon Yea, and Yo-Sung Ho. “Hole filling            method using depth based in-painting for view synthesis in            free viewpoint television and 3-d video.” Picture Coding            Symposium, 2009. PCS 2009. IEEE, 2009.

Disocclusions may be filled with special considerations for differentcases. According to some examples, disocclusions may be filled withpreviously mentioned methods such as, without limitation, imageinpainting (Patch match), and spatial interpolation.

For the case of inter-frame coding, conventional two-dimensional motioncompensation and estimation used in inter-frame prediction may onlyaccount for linear motion on a planar projection. One solution torectify this problem is to map the input vantage projection to anotherprojection map, such as cube map, that minimizes geometric distortionand/or favors straight-line motions. This procedure may be achieved bythe module that handles projection transform 3970 in FIG. 39.

In a manner similar to that of scalable video coding (SVC), the systemmay also make use of the tiles from one or more lower resolution layers(such as the first layer 3810 and/or the second layer 3820 of FIG. 38)to predict tiles on the higher resolution layer (such as the secondlayer 3820 and/or the third layer 3830 of FIG. 38). The inter-spatiallayer prediction scheme may provide progressive viewing capabilitiesduring downloading and streaming. It may also allow larger storagesavings by comparison with storage of each resolution independently.

Prediction Structure

The encoding prediction steps mentioned previously may be carried outaccording to a wide variety of techniques. Some examples will be shownand described in connection with FIGS. 40 through 44. By usinginter-frame and inter-vantage prediction, additional referencedependencies may be introduced in the coding scheme. These dependencesmay introduce higher decoding complexity and longer random accesslatency in the playback process. In these drawings, arrows betweenframes are used to illustrate dependencies. Where a first frame pointsto a second frame, data from the first frame will be used to predict thesecond frame for predictive coding.

Referring to FIGS. 40 through 44, various vantage encoding schemes 4000,4100, 4200, 4300, 4400 are depicted, according to certain embodiments.In the encoding schemes 4000, 4100, 4200, 4300, and 4400, the I-framesare keyframes that can be independently determined. The P-frames arepredicted frames with a single dependency on a previous frame, and theB-frames are predicted frames with more than one dependency on otherframes, which may include future and/or past frames. Generally, codingcomplexity may depend on the number of dependencies involved.

Of the coding structures of FIGS. 40 through 44, FIG. 44 may have thebest coding gain. In FIG. 44, all P-frames and B-frames have bothinter-frame and inter-vantage predicted frames as references. Thus,higher coding complexity and higher coding gain may both be present.

Many prediction structures may be used for inter-vantage prediction, inaddition to or in the alternative to those of FIGS. 40 through 44.According to one possible encoding scheme, every other vantage on thesampling grid may be uniformly selected as a prediction reference.Inter-vantage prediction can then synthesize the view between thereference vantages, as shown in FIGS. 45A and 45B.

Referring to FIGS. 45A and 45B, two encoding schemes 4500 and 4550 aredepicted, respectively, both having inter-vantage prediction, accordingto certain alternative embodiments. In these drawings, V(x,y,z)represents a vantage to be predicted through the use of surroundingvantages and/or a vantage that may be used to predict surroundingvantages. Vantages may be distributed throughout a viewing volume in anyof a wide variety of arrangements, including but not limited torectangular/cuboid arrangements, spherical arrangements, hexagonalarrangements, and non-uniform arrangements. The predictive principlesset forth in these drawings may be used in conjunction with any sucharrangement of vantages within a viewing volume.

FIG. 45A illustrates the encoding scheme with a low number ofintra-coded reference vantages. Advantageously, only one reference mayneed to be encoded. However, relying on a single prediction may notprovide the accuracy of multiple predictions, which may result in a dropin quality for the same bandwidth. Accordingly, there may be a tradeoffbetween bandwidth and the level of detail in the experience. Further,there may be tradeoff between decoding complexity and quality, with alarger number of dependencies increasing the decoding complexity. Yetfurther, a large number of dependencies may induce higher latency, whichmay necessitate buffering future frames.

To increase compression efficiency, the encoding scheme 4550 may alsodecrease the number of intra-coded reference vantages and have eachpredicted vantage predict other vantages as well. Therefore, theencoding scheme 4550 may create a chain of dependencies as shown in FIG.45B. If low decoding complexity and/or random access latency aredesired, a single reference scheme, such those of FIGS. 40 through 43,may be more suitable because they may trade away some of the compressionratio for lower latency and/or complexity.

Referring to FIGS. 46A and 46B, two encoding schemes 4600 and 4650 aredepicted, respectively, according to further alternative embodiments. Inthe encoding scheme 4600 and the encoding scheme 4650, asingle-reference vantage prediction structure may lay out athree-dimensional sampling grid. To optimize quality, a reference frameand its prediction dependencies may advantageously be chosen in arate-distortion optimized manner. Thus, inter-vantage prediction may becombined with intra-vantage prediction. Additionally or alternatively,inter-vantage prediction may be combined with inter-temporal and/orinter-spatial layering (not shown). The rate-distortion optimizationwill be shown and described in greater detail subsequently.

In some embodiments (not shown), a full inter-temporal/inter-vantageencoding scheme may be used. Such a scheme may provide optimum encodingefficiency, but may be relatively more difficult to decode.

Hierarchical Inter-Vantage Prediction Structure

In some embodiments, a hierarchical coding structure for inter-vantageprediction may provide a scalable solution to vantage compression. A setof vantages can be decomposed into hierarchical layers. The vantages inthe lower layer may be independently encoded and used as references forthe upper layers. The vantages in a layer may be predicted by eitherinterpolating or extrapolating the vantages views from the lower layers.

Such a coding scheme may provide scalability to address different rateand/or device constraints. Devices with less processing power and/orbandwidth may selectively receive, decode and/or store the lower layerswith a smaller viewing volume and/or lower vantage sampling density.

Referring to FIG. 54, a hierarchical coding scheme 5400 is depicted,according to one embodiment. The hierarchical scheme 5400 may vary thevantage sampling density and/or viewing volume to support differentsystem constraints of any clients. FIG. 54 provides a one-dimensionalview of the hierarchical coding scheme 5400, with exemplary operation ofthe hierarchical coding scheme 5400 illustrated in two dimensions inFIGS. 55A, 55B, 55C, and 55D.

Referring to FIGS. 55A, 55B, 55C, and 55D, a series of views 5500, 5520,5540, and 5560, respectively, depict the operation of the hierarchicalcoding scheme 5400 of FIG. 54 in two dimensions, according to oneembodiment. With reference to FIGS. 54 through 55D, all views of layer 1may be predicted by interpolation of all views from layer 0. Layer 2'sviews may be predicted by extrapolation of layers 0 and 1. Finally,layer 3 may be predicted by interpolation of layers 0, 1, and 2. Thehierarchical coding scheme 5400 may extend into three-dimensions. Thiswill be further shown and described in connection with FIGS. 56A, 56B,56C, and 56D, as follows. Further, FIGS. 54 through 55D are merelyexemplary; the system disclosed herein supports different layeringarrangements in three-dimensional space. Any number of coding layers maybe used to obtain the desired viewing volume.

Referring to FIGS. 56A, 56B, 56C, and 56D, a series of views 5600, 5620,5640, and 5660, respectively, depict the operation of the hierarchicalcoding scheme 5400 of FIG. 54 in three dimensions, according to anotherembodiment. As in FIGS. 55A through 55D, all views of layer 1 may bepredicted by interpolation of all views from layer 0. Layer 2's viewsmay be predicted by extrapolation of layers 0 and 1. Finally, layer 3may be predicted by interpolation of layers 0, 1, and 2.

Such hierarchical coding schemes may also provide enhancederror-resiliency. For example, if the client fails to receive, decode,and/or load the higher layers before the playback deadline, the clientcan still continue playback by just decoding and processing the lowerlayers.

Rate-Distortion-Optimized (RDO) Encoder Control with Decoding Complexityand Latency Awareness

The systems and methods of the present disclosure may utilize arate-distortion optimized encoder control that addresses differentdecoding complexity and latency demands from different content types andclient playback devices. For example, content with higher resolution ormore complex scenery might require higher decoding complexity. Contentstorage that does not need real-time decoding would exploit the highestcompression ratio possible without considering latency.

To estimate decoding complexity, the controller may map a clientdevice's capabilities to a set of possible video profiles with differentparameter configurations. The client device capabilities may includehardware and/or software parameters such as resolution, supportedprediction types, frame-rate, prediction structure, number ofreferences, codec support and/or other parameters.

Given the decoding complexity mapping and the switching latencyrequirement, the encoder control can determine the best possible videoprofile used for encoding. The latency can be reduced by decreasing theintra-frame interval and/or pruning the number of frame dependencies.

Decoding complexity can be reduced by disabling more complex predictionmodes, reducing playback quality, and/or reducing resolution. Using thechosen video profile, the controller can then apply Lagrangianoptimization to select the most optimal prediction structure andencoding parameters, for example, from those set forth previously.Exemplary Lagrangian optimization is disclosed in Wiegand, Thomas, andBernd Girod, “Lagrange multiplier selection in hybrid video codercontrol.” Image Processing, 2001. Proceedings. 2001 internationalConference on. Vol. 3. IEEE, 2001. An optimal encoder control may findthe optimal decision, {circumflex over (m)}, by the following Lagrangiancost function:{circumflex over (m)}=argmin _(mϵM) [D( l,m )+λ·R( l,m )_(□)]where l denotes the locality of the decision (frame-level or blocklevel), m denotes the parameters or mode decision, D denotes thedistortion, R denotes the rate and λ denotes the Lagrangian multiplier.

The encoder control may manage and find the optimal settings for thefollowing encoding parameters on a block or frame level:

-   -   Codec choice, such as JPEG, H.264/AVC, HEVC, VP8/9;    -   Reference selection from vantage bank, which stored all        reconstructed frames from past encoded frames, for        inter-spatial, inter-vantage and inter-frame predictions;    -   Prediction mode and dependencies;    -   Bitrates, quantization parameters and/or quality level;    -   I-frame interval or Group-of-Picture (GOP) size;    -   Resolution (spatial and/or temporal);    -   Frame-rate; and/or    -   Other codec specific parameters related to complexity and        quality, such as motion estimation types, entropy coding types,        quantization, post-processing filters, etc.        Compression/Decompression Codecs

In various embodiments, any suitable compression scheme and/or codec canbe used for encoding prediction residuals and encoder side information.The system may be compatible with image/texture-based and/or video-basedencoders, such as BC7, JPEG, H.264/AVC, HEVC, VP8/9 and others.Components, such as intra-frame prediction and inter-frame prediction,which exist in other codecs can be reused and integrated with the systemand method set forth herein.

Depth Channel Compression

In some embodiments, information regarding the depth of objects in ascene may be used to facilitate compression and/or decompression or tootherwise enhance the user experience. For example, a depth map, whichmay be a two-dimensional grayscale image with intensity indicative ofthe depth of objects, may be used. In general, depth channel compressionor depth map compression may advantageously preserve the mapping ofsilhouettes in the depth map to their associated color information.Image-based and/or video-based lossless compression techniques mayadvantageously be applied to the depth map data. Inter-vantageprediction techniques are applicable to depth map compression as well.Depth values may need to be geometrically re-calculated to anothervantage with respect to an origin reference. In a manner similar to thatof color, the (x,y) coordinate can be geometrically re-projected toanother vantage.

Extension to Other Data Representations

The techniques set forth above describe the application of spatialrandom access-enabled compression schemes to a vantage representation.Each vantage may consist of multi-channel color information, such asRGB, YUV and other color formats, and a single depth channel. Similartechniques can also be performed in connection with other forms of datarepresentation, such as layered depth images, as set forth in Shade,Jonathan, et al. “Layered depth images.” Proceedings of the 25th annualco since on Computer graphics and interactive techniques. ACM, 1998,epipolar plane image volumes, as set forth in Bolles, Robert C., H.Harlyn Baker, and David H. Marimont. “Epipolar-plane image analysis: Anapproach to determining structure from motion.”International Journal ofComputer Vision 1.1 (1987): 7-55, light field images, three-dimensionalpoint clouds, and meshes.

Temporal redundancies may be removed by tracking each data sample in thetemporal direction for a given representation. Spatial redundancies maybe removed by exploiting correlations between neighboring sample pointsacross space and/or layers, depending on the representation. Tofacilitate spatial random access similar to vantage-based tiling, eachsample from the corresponding layers may be grouped together accordingto their spatial location and/or viewing direction on a two-dimensional,three-dimensional, and/or other multi-dimensional space. Each groupingmay be independently encoded such that the viewer only needs to decodesamples from a subregion of a viewing volume when facing a givendirection with a given field-of-view.

Referring to FIGS. 57A, 57B, 57C, and 57D, a series of graphs 5700,5720, 5740, 5760, respectively, depict the projection of depth layersonto planar image from a spherical viewing range from a vantage,according to one embodiment. Specifically, the graph 5700 of FIG. 57Adepicts a top-down view of an input field-of-view projected on a simpledepth layer map. FIGS. 57B, 57C, and 57D depict the projection of thefirst, second, and third depth layers, respectively, on planar imagesfrom the spherical input field-of-view of FIG. 57A. Each depth layer maybe divided into tiles as shown. Such a layering scheme may be used toimplement the depth channel compression techniques set forth above. Thiscompression scheme may utilize a “Layered depth images” representation.This may be used as an alternative representation to represent athree-dimensional viewing volume, instead of using a vantage-basedsystem. In each depth layer, each pixel may contain color informationabout the three-dimensional scene for the corresponding depth. Forview-dependent lighting generation, each pixel may include extrainformation to describe how lighting varies between viewing angles. Togenerate a viewpoint, the viewpoint may be rendered directly.

System Architecture

Various system architectures may be used to implement encoding,decoding, and/or other tasks related to the provision of viewpoint videodata to a viewer. In some embodiments, the system may provide sixdegrees of freedom and/or full parallax in a three-dimensional viewingvolume. The system may be scalable to support different degrees ofimmersion. For example, all aforementioned techniques, such ashierarchical vantage prediction, spatial layers, and tiling, may supportscalability to different applications. Such a scheme may be scaled tosupport two-dimensional planar video, single viewpoint omnidirectionalthree-dimensional video, a virtual reality video system with onlyvertical or horizontal parallax, and/or systems with different degreesof freedom ranging from one degree of freedom to six degrees of freedom.To achieve such scaling, vantage density and vantage volume may bedecreased and/or the set of vantages and tiles that can be fetched togenerate a viewpoint may be limited. A hierarchical vantage scheme maybe designed to support different platforms, for example, a base layerthat supports one degree of freedom (a single vantage), a secondarylayer that supports three degrees of freedom with horizontal parallax (adisk of vantages), and a third layer that supports six degrees offreedom with full parallax (a set of all vantages in a viewing volume).Exemplary architecture will be shown and described as follows.

Tile Processing and Encoding

Referring to FIG. 47, a system 4700 for generating and compressing tilesis depicted, according to one embodiment. An input configuration file4710 may specify parameters such as the number of spatial layers neededand the size and location of the tiles for each spatial layer. Thetiling and spatial layering scheme may be as depicted in the tile 3800of FIG. 38. A vantage generator 4720 may generate all of the vantages,each of which may be omnidirectional as described above, and may containboth color and depth information, on a specified three-dimensionalsampling grid. The vantages may then be decomposed by a scaler 4730 intomultiple spatial resolution layers, as in the tile 3800. The scaler 4730may advantageously preserve correspondence between the edges of thedepth map and the color information to avoid any viewing artifacts.

For each spatial resolution layer (for example, for each of the firstlayer 3810, the second layer 3820, and the third layer 3830 of FIG. 38),a tile generator 4740 may crop the appropriate region and create thespecified tile(s). Each tile may then be compressed by an encoder 4750,which may be an encoder as described in any of the previous sections.For each tile, a metafile may be used to describe any additionalinformation, such as the codec used for compression, time segmentation,tile playback dependences and file storage, etc. The metadata may thussupport playback. The tiles and metadata may be stored in storage 4760.

Tile Decoding and Playback

Referring to FIG. 48, a system 4800 for tile decoding, compositing, andplayback is depicted, according to one embodiment. Tiles and/or metadatamay be retrieved from storage 4810. Based on the available data transferrate, the complexity budget, and/or the user's current viewinglocations, a tile server 4820 may relay a set of tiles that provide theoptimal viewing quality. After the tiles are decoded in decoders 4830, atile compositor 4840 may combine the fetched tiles together to form thecorresponding vantage views needed for rendering. The techniques tocombine the fetched tiles may include stitching, blending and/orinterpolation. Tiles can additionally or alternatively be generated byusing tiles from another spatial layer and/or neighboring tiles on thesame layer. The resulting viewpoint video data, which may include thecombined tiles, may be sent to a player 4850 for playback. When a tileis missing or corrupted, the playback system may use tiles from otherlayers and/or other tiles from the same layer for error concealment.Error concealment can be achieved by interpolation, upsampling,downsampling, superresolution, filtering, and/or other predictivetechniques.

In some embodiments, pause, fast-forward, and/or rewind functionalitymay be supported. The system may perform spatial-temporal access on thetiles at the same time during fast-forward and rewind, for example, byfast-forwarding or rewinding while the viewer's head is moving. Theplayback tile may continue to stream and/or decode the tiles spatiallyand temporally while a user is rewinding or fast-forwarding. A similarfeature may be implemented to facilitate pausing playback.

FIG. 49 is a diagram 4900 depicting how a vantage view may be composed,according to one embodiment. A viewport 4910 illustrates the FoV of theviewer, which may be selected by viewer via motion of his or her head,in the case of a virtual reality experience. Tiles 4920 that are atleast partially within the central region of the viewport 4910 may berendered in high resolution. Thus, these tiles may be fetched from ahigh-resolution layer (for example, the third layer 3830 of FIG. 38).

To reduce complexity, tiles 4930 outside of the viewport 4910 may befetched from the lower resolution layers (for example, the first layer3810 of FIG. 38). Depending on the content and viewing behavior, thetile server may also fetch tiles from lower resolution layers in lessperceptible regions of the viewport 4910.

In the example of FIG. 49, the vantage may be projected to anequirectangular map. The tiles 4940 on top of the viewing area may befetched from a mid-resolution layer (for example, the second layer 3820of FIG. 38) because the top region of an equirectangular map is oftenstretched and over-sampled.

Referring to FIG. 50, a diagram 5000 depicts the view of a checkerboardpattern from a known virtual reality headset, namely the Oculus rift. Asshown, there is significant viewing distortion near the edges of the FoVof the head-mounted display. Such distortion may reduce the effectivedisplay resolution in those areas, as illustrated in FIG. 50. Thus, theuser may be unable to perceive a difference between rendering withlow-resolution tiles and rendering with high-resolution tiles in thoseregions. Returning briefly to FIG. 49, tiles 4950 at the bottom regionof an equirectangular map may be fetched from a low-resolution layer(for example, the first layer 3810 of FIG. 38). Similarly, if aparticular portion of a scene is not likely to command the viewer'sattention, it may be fetched from a lower resolution layer.

When the scene inside a tile is composed of objects that are far away,the variations in view-dependent lighting and occlusions are verylimited. Instead of fetching a set of four or more vantage tiles forrendering the view, the system might only need to fetch a single tilefrom the closest vantage. Conversely, when the scene inside a tile hasone or more objects that are close to the viewpoint, representation ofthose objects may be more realistic if tiles from all four (or evenmore) vantages are used for rendering the view on the display device.

Through multi-spatial layer composition, a system and method accordingto the present disclosure may provide flexibility to optimize perceptualquality when the system is constrained by computing resources such asprocessing power, storage space, and/or bandwidth. Such flexibility canalso support perceptual rendering techniques such as aerial perspectiveand foveated rendering.

Notably, the system 4700 of FIG. 47 and/or the system 4800 of FIG. 48may be run locally on a client machine, and/or remotely over a network.Additional streaming infrastructure may be required to facilitate tilestreaming over a network.

Content Delivery

In various embodiment, the system and method may support different modesof content delivery for immersive videos. Such content delivery modesmay include, for example and without limitation:

-   -   Compressed video data storage on physical storage medium;    -   Decompressed video data downloaded to client device;    -   Compressed video data downloaded to client device with offline        decompression; and    -   Video data streamed to client device.        Compressed Volumetric Video Data Storage on Physical Storage        Medium

When a physical storage medium is available, the compressed volumetricvideo data may be stored on and retrieved from a local physical storagemedium and played back in real-time. This may require the presence ofsufficient memory bandwidth between the storage medium and the system'sCPU and/or GPU.

Decompressed Video Data Downloaded to Client Device

The compressed video data may be packaged to support contentdownloading. The compression and packaging may be selected to meet theclient device's complexity and storage capabilities. For a less complexdevice, such as a smartphone, lower resolution video data and/or lesscomplex video data may be downloaded to the client device. In someembodiments, this may be achieved using the scalability techniquesdescribed previously.

Compressed Volumetric Video Data Downloaded to Client Device withOffline Decompression

When the file size of the volumetric video data or download time is aconcern, the system can remove the decoding complexity constraint andcompress the file stream by using the best available compressionparameters. After a client device downloads the compressed package, theclient can then decode the package offline and transcode it to anothercompression format that can be decodable at real-time, usually at thecost of creating a much larger store of compressed volumetric videodata.

Video Data Streamed to Client Device

A tiling scheme with multiple resolution layers, as described above inconnection with FIG. 38 and elsewhere above, may offer a scalable systemthat can support any arbitrary viewings from a large number of usersinside a video volume at the same time. A tiling scheme may help reducestreaming bandwidth, and a spatial layering scheme may help meetdifferent client limitations in bandwidth and decoding complexity. Alayering scheme may also provide concealment to spatial random accesslatency and any network packet losses or data corruption.

Method for Capturing Volumetric Video Data

The systems described above may be used in conjunction with a widevariety of methods. One example will be shown and described below.Although the systems and methods of the present disclosure may be usedin a wide variety of applications, the following discussion relates to avirtual reality application.

Referring to FIG. 51, a method 5100 is depicted for capturing volumetricvideo data, encoding the volumetric video data, decoding to obtainviewpoint video data, and displaying the viewpoint video data for aviewer, according to one embodiment. The method 5100 may start 5110 witha step 5120 in which the volumetric video data is captured. This may bedone, for example, through the use of a tiled camera array such as anyof those described above.

In a step 5130, vantages may be distributed throughout the viewingvolume. The viewing volume may be a designated volume, from within whichthe captured scene is to be viewable. The vantages may be distributedthroughout the viewing volume in a regular pattern such as athree-dimensional grid or the like. In alternative embodiments, thevantages may instead be distributed in a three-dimensional hexagonalgrid, in which each vantage is equidistant from all of its immediateneighbors. Such an arrangement may approximate a sphere. Vantages mayalso be distributed non-uniformly across the three-dimensional viewingvolume. For example, regions of the viewing volume that are more likelyto be selected as viewpoints, or from which the scene would beneficiallybe viewed in greater detail, may have comparatively more vantages.

In a step 5140, the volumetric video data may be used to generate videodata for each of the vantages. For any given vantage, the correspondingvideo data may be usable to generate a view of the scene from aviewpoint located at the vantage.

In a step 5150, user input may be received to designate a viewpointwithin the viewing volume. This may be done, for example, by a viewerpositioning his or her head at a location corresponding to theviewpoint. The orientation of the viewer's head may be used to obtain aview direction along which the view from the viewpoint is to beconstructed.

In a step 5160, a subset of the vantages nearest to the viewpoint may beidentified. The subset may be, for example, the four vantages closest tothe viewpoint, which may define a tetrahedral shape containing theviewpoint, as described previously. In step 5170, the video data for thesubset of vantages may be retrieved.

In a step 5180, the video data from the subset of vantages may becombined together to yield viewpoint video data representing the view ofthe scene from the viewpoint, from along the view direction. The videodata may be interpolated if the viewpoint does not lie on or adjacent toone of the vantages.

Further, various predictive methods may be used, as set forth above, tocombine future video data from the viewpoint and/or future video datafrom proximate the viewpoint. Such predictive methods may be used togenerate at least a portion of the viewpoint video data for a futureview from any combination of the viewpoint, an additional viewpointproximate the viewpoint, the view direction, an additional viewdirection different the view direction. Thus, if the viewer actuallydoes turn his or her head in alignment with the viewpoint and viewdirection pertaining to the predicted viewpoint video data, thepredicted viewpoint video data may be used to streamline the stepsneeded to display the scene from that viewpoint, along that viewdirection. Additionally or alternatively, the playback system maypredict one or more viewing trajectories along which the viewer islikely to move his or her head. By predicting the viewing trajectories,the system may pre-fetch the tiles to be decoded and rendered tominimize viewing latencies.

Additionally or alternatively, predictive methods may be used to predictviewpoint video data without having to receive and/or process theunderlying video data. Thus, tighter bandwidth and/or processing powerrequirements may be met without significantly diminishing the viewingexperience.

In a step 5190, the viewpoint video data may be transmitted to theclient device. Notably, this is an optional step, as the steps 5150,5160, 5170, and 5180 may be optionally performed at the client device.In such an event, there may be no need to transmit the viewpoint videodata to the client device. However, for embodiments in which the step5180 is carried out remotely from the client device, the step 5190 mayconvey the viewpoint video data to the client device.

In a step 5192, the viewpoint video data may be used to display a viewof the scene to the viewer, from the viewpoint, with a FoV orientedalong the view direction. Then, in a query 5194, the method 5100 maydetermine whether the experience is complete. If not, the method 5100may return to the step 5150, in which the viewer may provide a newviewpoint and/or a new view direction. The steps 5160, 5170, 5180, 5190,and 5192 may then be repeated to generate a view of the scene from thenew viewpoint and/or along the new view direction. Once the query 5194is answered in the affirmative, the method 5100 may end 5196.

Video Stream Storage Method

According to some embodiments, a volumetric video stream may be dividedand stored in a manner that facilitates rapid retrieval of the necessarydata to be delivered to the viewer at a given point in time. Since avirtual reality or augmented reality experience is dependent upon theviewpoint and view orientation selected by the viewer at each point intime, the entire video stream need not be delivered to the viewer.Hence, it may be advantageous for the storage method and schema toprovide ready access to the blocks of data that will be needed to rendera particular viewpoint and/or view orientation.

In some embodiments, a hierarchical storage schema may be used. Thus, avideo stream depicting a scene may be divided into units, and the unitsmay be divided into sub-units. These divisions may be based on variousfactors, which may include, but are not limited to:

-   -   Time segmentation: division of the video stream into units that        are sequences of successive frames in time;    -   Viewpoint segmentation: division of the video stream into units,        each of which represents a viewpoint from which the scene can be        viewed; and    -   View orientation segmentation: division of the video stream into        units, each of which represents a particular view orientation        along which the scene can be viewed.

These are merely exemplary; other forms of segmentation may be used todivide a video stream for rapid access for a virtual reality oraugmented reality experience. In some embodiments, the video stream maybe divided hierarchically, with one form of segmentation used to dividethe video stream into units, and then another form of segmentation usedto divide each of the units into sub-units. Further division ofsub-units into sub-sub-units may optionally be used. One exemplarymethod will be shown and described in connection with FIG. 58.

Referring to FIG. 58, a method 5800 is depicted for storing a videostream, which may be volumetric video data to be used for a virtualreality or augmented reality experience, according to one embodiment.The method 5800 may be performed with any of the hardware mentionedpreviously, such as the post-processing circuitry 3604, memory 3611,user input 3615, and/or other elements of a post-processing system 3700as described in the above-referenced U.S. patent applications.

The method 5800 may start 5810 with a step 5120 in which the volumetricvideo data is captured. This may be done, for example, through the useof a tiled camera array such as any of those described above. In a step5130, vantages may be distributed throughout the viewing volume. In astep 5140, the volumetric video data may be used to generate video datafor each of the vantages. The step 5120, the step 5130, and the step5140 may all be substantially as described above in connection with themethod 5100 of FIG. 51.

In a step 5820, the video stream may be divided into units. This may bedone using any form of segmentation, including but not limited to thetime segmentation, viewpoint segmentation, and view orientationsegmentation methods described previously. The units may be of anydesired size.

In a step 5830, each of the units of the video stream may be dividedinto sub-units. Again, any form of segmentation may be used. The sameform of segmentation applied in the step 5820 may optionally be used.Thus, for example, if the video stream is segmented based on time,performance of the step 5830 may involve further segmenting each timesegment into shorter time segments. However, it may be advantageous toapply a different form of segmentation in the step 5830 to facilitaterapid location and retrieval of the portion of the video streamapplicable to a particular point in time, a particular viewpoint, and/ora particular view orientation.

In a step 5840, each of the sub-units may be divided into sub-sub-units.It may be advantageous to apply a different form of segmentation fromthose applied in the step 5820 and the step 5830. Thus, access to theportion of the video stream that is needed at a particular stage of theviewing experience may be obtained at a still more granular level. Thestep 5840 is optional; if desired, the video stream may only besegmented once or twice. Of course, further segmentation may also beapplied any number of times. Thus, for example, the video stream may befurther segmented into successively smaller elements.

There may be a significant portion of the video stream that isredundant. For example, views of a static background portion of a scenemay be substantially unchanged through several frames of the videostream. In order to reduce the storage volume, processing workload, andbandwidth requirements of the system that delivers the video stream forthe virtual reality experience, it may be desirable to avoid storingduplicative portions of the video stream.

Thus, in a step 5850, duplicate units, sub-units, and/or any smallerelements, as applicable, may be identified within the video stream. Thismay be done, for example, with the aid of a user, or via automatedcomparison techniques that compare units, sub-units, and/or smallerelements with each other to identify likely duplicates. In someembodiments, this comparison may be based on comparing tiles with eachother, where each tile is the portion of a vantage viewable through alimited field-of-view, as described above and in the applicationsincorporated by reference herein.

Notably, the step 5850 may not only identify identical elements withinthe video stream, but may identify elements that are similar enough tobe perceptually similar for further reduction of storage spacerequirements. For example, various metrics such as structural similarity(SSIM), peak signal-to-noise ratio (PSNR), sum of absolute differences,sum of squared differences, and the like may be used to compare GOP's,vantages, tiles, frames, and/or the like to determine whethernon-identical items are perceptually similar enough to be stored as asingle element. The single element to be stored may be any one of theperceptually-similar elements. Alternatively, the single element may begenerated by combining two or more of the perceptually-similar elementstogether. For example, averaging and/or application of a median filtermay be applied across the perceptually similar elements to generate thecombined element to be stored. The address table(s) may simply point tothe combined element to access any of the perceptually similar elements,which need not be stored.

In a step 5860, the units (including any sub-units, sub-sub-units,and/or smaller elements) may be stored in one or more files. Forduplicate units identified in the step 5850, rather than storing theduplicate unit, sub-unit, or smaller element, the start address of thematching unit, sub-unit, or smaller element may be stored. Thus,redundancy in the stored video stream may be reduced.

In a step 5870, hierarchical lookup tables, or “offset tables,” may begenerated for the units, sub-units, and/or smaller elements. Such offsettables may function as tables of contents, providing the start addressesfor the units, sub-units, and/or smaller elements, as applicable. In astep 5880, the offset tables may be stored, for example, in the one ormore files in which the corresponding units were stored in the step5860.

Various other parameters may be useful for retrieval, decompression,and/or rendering the video stream for the viewing experience. Theseparameters may be stored in a step 5890, for example, in the one or morefiles in which the corresponding units and/or offset tables were storedin the step 5860 and the step 5880. The method 5800 may then end 5896.

The various steps of the method 5800 of FIG. 58 are merely exemplary. Inalternative embodiments, they may be re-ordered or revised to omit oneor more steps, replace one or more steps with alternatives, and/orsupplement the steps with other steps not specifically shown anddescribed herein.

In one particular embodiment, the step 5820 may involve timesegmentation, in which the video stream is broken into groups ofpictures (GOP's), each of which is a sequence of successive frames intime. Each GOP may include all viewpoints and view orientations withinthe time frame pertinent to the GOP.

The step 5830 may involve viewpoint segmentation, in which each of theGOP's is divided into vantages, with each vantage being the subset ofthe GOP representing the view of the scene from a particular viewpoint,or vantage, as described previously and in the applications incorporatedby reference herein. The data for each vantage may include all vieworientations along which the scene can be viewed from that vantage.Thus, each vantage may contain a 360°/180° projected view of the scenefrom a particular viewpoint.

The step 5840 may involve view orientation segmentation, in which eachof the vantages is divided into tiles. Each tile may be the subset ofthe vantage data applicable to a particular view orientation, or fieldof view, as described previously and in the applications incorporated byreference herein.

This schema may provide ready access to the particular tiles needed torender a view at any point in time within the video stream. Varioussteps of the method 5800 will be shown and described, along withassociated data structures, in greater detail with reference to thisexemplary embodiment, as follows.

File Architecture

A wide variety of file architectures may be used in conjunction with themethod 5800 of FIG. 58. In some embodiments, the units, offset tables,and parameters may all be stored in a single file. In the alternative,these elements may be broken into multiple files, for example, based onany of the segmentation methods described above. One exemplary filestructure will be shown and described in connection with FIG. 59.

FIG. 59 depicts a file 5900 that may be used to store audio and/or videodata for a virtual reality or augmented reality experience, according toone embodiment. As shown, the file 5900 may have a code 5910, a globalheader size 5920, a global header 5930, a pad 5940, metadata 5950,captured scene data 5960, content parameter data 5970, an audio payload5980, and a video payload 5990. Each of these elements will be furtherdescribed in Table 1, below:

TABLE 1 Length and Description of File Elements Type Length DescriptionCode 4 Bytes A four-character code used to uniquely identify the virtualreality/augmented reality data format type Global Header 4 Bytes Size ofglobal header in bytes Size Global Header Variable Specifies the offsetlocation and/or length of each payload type. Global information that ishelpful for decoding, playback and/or rendering of the entire filestream. Stored in JSON format. Padding Variable Padding used by globalheader Metadata Variable Binary data that cannot be stored in globalheader as JSON format, but contains global information and/orcodec-specific parameter sets required for decoding, playback and/orrendering of the entire file stream. For example, when JPEG codec isused for compression color information, this field may contain JPEGcodec- specific metadata, such JPEG header, quantization tables, and/orHuffman tables. May also include information such as playback auxiliaryinformation, rendering parameters, and/or subtitles. Captured SceneVariable Stored in JSON format. May contain scene configuration,rendering information and/or vantage parameters such as location, cameraparameters, field of view and dimension Content Variable Stored in JSONformat. May specify playback and Parameters rendering options andbehavior, such as gray room transition, viewing volume behavior, viewingvolume rotation and/or offset. Audio Variable Audio payload, which maybe compressed using any audio codecs, such as Facebook TBE Audio formatVideo Variable Video payload, which may be compressed using any videocodecs, such as BPTC/BC7, JPEG, HEVC, modified and/or combined versionsof the foregoing, etc

This file structure is merely exemplary. Other architectures may beused. Some of the elements of the file 5900 will be shown and describedin greater detail, as follows. These descriptions are also exemplary;those of skill in the art will recognize that alternative datastructures may be used within the scope of the present disclosure.

Global Header 5930

The global header 5930 may specify the offset location and/or length ofeach payload type and/or any global information that is required fordecoding, playback and/or rendering of the entire file stream. Theglobal header 5930 may be stored in JSON format to facilitatedevelopment and maintenance flexibility. The global header 5930 maycontain various elements, which will be shown and described in Table 2,below:

TABLE 2 Data Types and Descriptions of Global Header Elements Field DataType Description VersionMajor uint32 Version maintenance field.VersionMinor uint32 VersionPatch uint32 NumberOfGops uint32 Number ofgroups of pictures (GOPs) in the file stream. NumberOfFrames uint32Number of temporal frames in the file stream. VideoDataLength uint64Total data length of video payload. CapturedSceneJsonOffset uint64 Fileoffset to captured scene data in bytes. CapturedSceneJsonLength uint64Total data length of captured scene data. ContentParamsJsonOffset uint64File offset to content parameter data in bytes. ContentParamsJsonLengthuint64 Total data length of content parameter data. AudioOffset uint64File offset location to audio data. AudioLength uint64 Total data lengthof audio data. MetaDataFileOffset uint64 File offset to metadata.MetaDataLength uint64 Total data length of metadata. NumberOfVantagesuint32 Number of vantages in the file stream. NumberOfFramesPerGopuint32 Number of temporal frames per GOP. NumberOfTilesInX uint32 Numberof tiles in columns. NumberOfTilesInY uint32 Number of tiles in rows.VantageRelativeOffsetFieldInBytes¹ uint32 Bytes size used to representvantage-relative offset, which may be the file offset location of agiven vantage-relative to the end of each GOP header. Default: 6 bytesTileRelativeOffsetFieldInBytes uint32 Bytes size used to representtile-relative offset, which may be the location offset of a giventile-relative to the corresponding vantage start location. Default: 4bytes TileDataLengthFieldInBytes uint32 Bytes size used to represent thedata length a tile. Default: 2 bytes GopTableFileOffset uint64 Fileoffset location to the video pay's GOP table in bytes

Regarding the field “TileRelativeOffsetFieldInBytes,” the global header5930 may specify the size, in bytes, of each type of offset address,such as vantage-relative offset and tile-relative offset. By having avariable byte size, the offset table may be expanded or reduced in sizeto accommodate content with different size in vantages and differenttime length, i.e., still frame room-scale content versus seated viewingvolume. As the viewing volume and the video time length grow, the offsetaddressable byte sizes may be increased, thus increasing the size of GOPoffset table, vantage offset table and tile offset table, which will beshown and described below.

Captured Scene Data 5960

The captured scene data 5960 may contain scene configuration, viewingparameters and/or vantage information, such as location, cameraparameters, view frustum parameter, field of view, and texturedimension. Some of this information may be generated during the vantagegeneration process, as in the step 5140 of the method 5100 of FIG. 51.Additional information, such as compression format, may be added to thecaptured scene data 5960 after completion of the vantage generationprocess. The captured scene data 5960 may be stored in JSON format thefile 5900. Exemplary captured scene parameters are set forth below:

{ “directive”: “GenerateHeadsetImages”, “rigConfig”: “plane”,“numCamerasPerEvenRow”: 3, “numCamerasPerOddRow”: 4, “perCameraLeft”:0.088, “perCameraRowUp”: 0.078, “perCameraOddRowLeft”: −0.044,“compression”: “Ivr”, “inputImageFileNameIndexFieldWidth”: 6,“outputImageStoreIfNoSrcImages”: true, “outputImageJpegQuality”: 100,“inputImageDepthCodePixelValidity”: true, “cuboidGridVantagePattern”: {“pCenter”: [ 0.0, 0.0, 0.0 ], “vPitch”: [ 0.1, 0.1, 0.1 ] }, “vantages”:[ { “index”: 61, “imageWidth”: 640, “imageHeight”: 640, “fov”: {“projectionType”: “equirectangular”, “upDeg”: 23.0, “downDeg”: 23.0,“leftDeg”: 23.0, “rightDeg”: 23.0, “zNear”: 0.10000000149011612, “zFar”:100.0 }, “rigFromCamera”: [ 1.0, 0.0, 0.0, −0.10000000149011612, 0.0,1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0 ],“cameraFromRig”: [ 1.0, 0.0, 0.0, 0.10000000149011612, 0.0, 1.0, 0.0,0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0 ] }, { “index”: 62,“imageWidth”: 640, “imageHeight”: 640, “fov”: { “projectionType”:“equirectangular”, “upDeg”: 23.0, “downDeg”: 23.0, “leftDeg”: 23.0,“rightDeg”: 23.0, “zNear”: 0.10000000149011612, “zFar”: 100.0 },“rigFromCamera”: [ 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0,1.0, 0.0, 0.0, 0.0, 0.0, 1.0 ], “cameraFromRig”: [ 1.0, 0.0, 0.0, 0.0,0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0 ] } ] }Content Parameter Data 5970

The content parameter data 5970 may specify playback and renderingoptions and behavior, such as gray room transition, viewing volumebehavior, viewing volume rotation and/or offset, and the like, and maybe stored in JSON format. In some embodiments, the content parameterdata 5970 may include one or more of the following:

-   -   Camera Height/Drop-in Height/Floor Tracking—specifies the limits        of the viewer's position relative to elements of the        environment, such as the floor;    -   Draw Gray VR Room—may be used to further limit viewer motion,        for example, to a single room, i.e., for testing purposes;    -   Transition into Gray VR Room—may be used to provide a gray        viewing area, for example, as a temporary stand-in for computer        generated elements;    -   Viewing Volume Behavior—may be used to specify the boundaries of        the viewing volume, which may be cubic, spherical, rectangular        prismatic, or any other 3D shape, and may be used to specify        transitions out of the viewing volume in which viewpoint motion        is dampened and/or completely clamped;    -   Viewing Volume Visualization—may be used to display a mesh        representative of the viewing volume, i.e., for testing        purposes—the mesh may disappear as the viewpoint approaches the        viewing volume boundary;    -   View Volume Handling—may include elements of Viewing Volume        Behavior and/or Viewing Volume Visualization, and/or may be used        to specify what occurs when the viewpoint approaches the        boundary of the viewing volume, control visualization of the        viewing volume, and/or even selectively allow the viewpoint to        depart the viewing volume, i.e., for testing purposes, with the        possibility of scene fade-out outside the viewing volume;    -   Viewing Volume Rotation and Position Offsets—may be used to add        rotational and/or position offsets to the viewing volume, for        example, in meters;    -   Sparse Frames—may specify how sparse frames are handled, for        example, in gaps in the sequence of frames that make up the        video stream—gaps may be filled by repeating the frame at the        end of the gap for each missing frame;    -   Sharpening—may be used to enable or disable sharpening for        playback and/or control the amount of sharpening to be applied;        and    -   FXAA—may be used to enable or disable FXAA for playback and/or        control parameters for FXAA application, such as:        -   Depth Threshold, which specifies the value, in depth units,            of what is considered an edge due to depth;        -   Edge Detect Factor, which specifies the luminance difference            threshold that determines what is considered an edge based            on luminance differential;        -   Smoothing Multiplier, which specifies how much smoothing is            to be applied in the course of FXAA application; and        -   Antialiasing, which specifies the degree to which            antialiasing is to be applied.

These are merely exemplary. Those of skill in the art will recognizethat a wide variety of parameters may be specified in the contentparameter data 5970, in addition to and/or in place of the foregoing.

Video Payload 5990 and Offset Tables 6010

The video payload 5990 may be stored in any suitable format. In someembodiments, segmentation of the video stream may result in thegeneration of a bitstream with the structure depicted in FIG. 60.

FIG. 60 is a representation 6000 of a bitstream that may be used for thevideo payload 5990, according to one embodiment. As shown, a GOP offsettable 6010 may be used to provide the start address for each of m GOPs6016, into which the video stream has been divided. The start address ofeach of the GOPs 6016 may be provided as a GOP offset 6018, for example,in the GOP offset table 6010.

Each of the GOPs 6016 may have a GOP header 6020 and a GOP payload 6022.The GOP header 6020 may contain a vantage offset table 6030, a tileoffset table 6032, and optionally, other GOP data 6034. The GOP payload6022 may contain n vantages 6036, into which each of the GOP's 6016 hasbeen divided. The start address of each of the vantages 6036 may beprovided as a vantage offset 6038 in the vantage offset table 6030.

Each of the vantages 6036 may contain frames 6046 covering the block oftime pertinent to the GOP 6016 that contains the vantage 6036.Specifically, each of the vantages 6036 may contain t frames 6046, eachof which is a single video frame, or a single image, for that vantage6036. Each of the frames 6046 may contain p tiles 6056. Each of thetiles 6056 may represent the subset of the frame 6046 of the vantage6036 along one or more particular view orientations, or fields of view.The start address of each of the tiles 6056 may be provided as a tileoffset 6058 in the tile offset table 6032.

More detail regarding each of the elements of the video payload 5990 isprovided in Table 3, below:

TABLE 3 Descriptions of Video Payload Elements Description VantageVirtual reality or augmented reality video with six degrees of 6036freedom may be represented by creating a 3D sampling grid over theviewing volume, in which each point is a vantage 6036. Each vantage 6036may contain a 360°/180° projected view of the scene at a givencoordinate in the sampling grid. It may contain color, texture and/ordepth information. Frame A frame 6046 may be coded still texture and/ordepth information 6046 for a vantage 6036. Each frame 6046 may bedecomposed to form many tiles 6056. Tile A tile 6056 may be the basicatomic unit in the video payload 6056 5990. A tile 6056 may be arectangular subdivision within a frame 6046 of a vantage 6036. Each tile6056 may contain the compressed payload of the color and/or depthchannels. The format of the compressed payload bitstream may bedetermined by the codec used. In some embodiments, BPTC/BC7 may be usedthe default codec. Different codecs, such as JPEG, LZ4, H.264,combinations and/or modifications thereof, etc, may alternatively beused. GOP A GOP 6016 may be a group of successive frames within a coded6016 video stream. Each coded video stream may consist of successiveGOPs 6016. Each GOP 6016 may have temporal and/or spatial codingdependencies between each of the tiles 6056 of a single vantage 6036.Inter-vantage dependencies may optionally be supported in someembodiments. By way of example, each GOP 6016 may contain 30 to 40successive frames 6046 for each vantage 6036. GOP offset table The GOPoffset table 6010 may contain the 8-byte relative offset 6010 betweenthe end address of GOP offset table 6010 and the start address of theGOP header 6020 for each of the GOPs 6016. Vantage offset table Thevantage offset table 6030 may list the relative offsets between 6030 theend address of the GOP header 6020 to the start address of each vantage6036 in the video payload 5990. The default offset size may be 6 bytes.Tile offset table The tile offset table 6032 may list the relativeoffsets between the 6032 start address of each vantage 6036 to thebeginning address of each tile 6056 in the video payload 5990, as wellas the data size of each tile 6056. The default offset size may be 4bytes and the data size may be addressable by 2 bytes.

Further exemplary parameters of the various elements of the videopayload 5990 will be set forth below, and in Table 4. These areexemplary; those of skill in the art will recognize that a wide varietyof variations may be provided, in different embodiments.

GOP Offset address size: 8 bytes

VantageRelativeOffsetFieldInBytes: 6 bytes

TileRelativeOffsetFieldInBytes: 4 bytes

TileDataLengthFieldInBytes: 2 bytes

Vantage offset header size: 6 bytes per vantage

Tile header size: (4+2) 6 bytes per tile

Maximum tile size: 65,536 bytes

Maximum single vantage GOP size: 4 GB

Maximum GOP size: 281 TB

Maximum file size: 16 EB

TABLE 4 GOP Parameters Time Duration Number of Number GOP Header Size(min) Vantages of Tiles (GB) 5 311 128 6.5 5 5000 128 104 3 311 128 3.91 311 128 1.3

In some embodiments, addressing for sub-units can even be subdividedinto channels. A channel may contain color, depth, alpha, coverageand/or other scene information. Each channel may have its own addresslocation. This will be further shown and described in connection withFIG. 61.

FIG. 61 is a representation 6100 of a bitstream that may be used for thevideo payload 5990, according to another embodiment. The bitstream ofFIG. 61 may have a configuration similar to that of the bitstream ofFIG. 60, except that in FIG. 61, color and depth channels may besubdivided.

As in FIG. 60, a GOP offset table 6010 may be used to provide the startaddress for each of m GOPs 6116, into which the video stream has beendivided. The start address of each of the GOPs 6116 may be provided as aGOP offset 6018, for example, in the GOP offset table 6010.

Each of the GOPs 6116 may have a GOP header 6120 and a GOP payload 6022.The GOP header 6120 may contain a vantage offset table 6030, a coloroffset table 6132, a depth offset table 6133, and optionally, other GOPdata 6034. The GOP payload 6022 may contain n vantages 6136, into whicheach of the GOP's 6116 has been divided. The start address of each ofthe vantages 6136 may be provided as a vantage offset 6038 in thevantage offset table 6030.

Each of the vantages 6136 may contain frames 6146 covering the block oftime pertinent to the GOP 6116 that contains the vantage 6136.Specifically, each of the vantages 6136 may contain t frames 6146, eachof which is a single video frame, or a single image, for that vantage6136. Each of the frames 6146 may contain p color tiles 6156 and p depthtiles 6166. Each of the color tiles 6156 may contain the color datarepresenting the subset of the frame 6146 of the vantage 6136 along oneor more particular view orientations, or fields of view. Similarly, eachof the depth tiles 6166 may contain the depth data representing thesubset of the frame 6146 of the vantage 6136 along fields of viewcorresponding to those of the color tiles 6156. Thus, each of the depthtiles 6166 may provide depth information that can be matched to thecolor information of one of the color tiles 6156. The start address ofeach of the color tiles 6156 may be provided as a color tile offset 6058in the color tile offset table 6132. Similarly, the start address ofeach of the depth tiles 6166 may be provided as a depth tile offset 6168in the depth tile offset table 6133.

Each of the segments or units set forth above may be stored in one ormore different types of memory across the system, including but notlimited to CPU memory, GPU memory, hard drive storage, network storage,distributed network storage, memories across multiple nodes, and thelike. Each lookup table, such as the GOP file offset table 6010, thevantage offset table 6030, the tile offset table 6032, the color tileoffset table 6132, and the depth offset table 6133, may utilize avirtual addressing scheme that provides a reference to the correctphysical address location across the multiple memory storage types foreach unit.

In order to optimize read access performance, sub-units that are morefrequently accessed may advantageously be prioritized to be stored inmemory units with higher read access speed. A prioritization scheme forstoring sub-units across different memory types may be optimizedaccording to the following elements:

-   -   Objective function: to minimize the average read access time        based on the estimated access frequency of each subunit during        playback; and    -   Constraints:        -   Memory size for each sub-unit;        -   Available memory capacity of each memory type; and        -   Data access speed of each memory type.

If desired, address tables, such as the GOP file offset table 6010, thevantage offset table 6030, the tile offset table 6032, the color tileoffset table 6132, and the depth tile offset table 6133, may becompressed through the use of dictionary-type compression algorithms.Any algorithm known in the art may be used. In some embodiments,compression algorithms such as Lempel-Ziv, LZ4, LZO and LZJ may be used.

Referring to FIGS. 62A, 62B, and 62C, a GOP offset table 6010, a vantageoffset table 6030, and a tile offset table 6032 are depicted,respectively, according to one embodiment. As shown, the GOP offsettable 6010 may have a series of GOP address offsets 6200. Similarly, thevantage offset table 6030 may have a series of vantage address offsets6220. The tile offset table 6032 may be broken down by vantage 6036,frame 6046, and tile 6056.

For example, the tile offset table 6032 may have, for the first vantage6036 and the first frame 6046, address offsets 6240 for each of the ptiles 6056 of the frame 6046. The tile offset table 6032 may haveaddress offsets for another p tiles 6056 of the second frame 6046, andso on, for all t frames 6046. The same structure may be present for eachof n vantages 6036. The tile offset table 6032 may thus have addressoffsets 6240 for p tiles 6056 for each of t frames 6046, for each of nvantages 6036.

As mentioned previously, redundant portions of the video stream need notbe stored more than once. For example, tiles 6056 that cover the staticbackground of a scene may advantageously be encoded only once to reducebit-rate requirements. To refer a skipped tile 6056 to the correct tilereference, the system can simply write the address offset 6240 of thereference tile 6056 in place of the address offset 6240 for theduplicative tile 6056 on the tile offset table 6032.

In addition or in the alternative to temporal references, an addressoffset 6140 within the tile offset table 6032 may refer to any tiles6056 that have been previously encoded within the same vantage 6036 andGOP 6016. Therefore, spatial redundancies (i.e., tiles 6056 that are atdifferent view orientations, and yet are substantially the same) mayalso exploited inside a vantage 6036 to further reduce bit-raterequirements.

Audio Payload

The audio payload 5980 may include audio to accompany the video payload5990. The audio payload 5980 may be stored in any suitable format, whichmay include, for example, Facebook Audio 360, formerly known as TBEformat, which supports positional tracking audio for an immersive VRexperience. More information regarding the Facebook Audio 360 format maybe obtained athttps://s3.amazonaws.com/fb360-spatial-workstation/RenderingEngine/1.0.0/FB360_Rendering_SDK_1.0.0.zip.Other audio codec formats can additionally or alternatively be used inthe audio payload, as long as the playback client supports the format.

File Generation

To generate the file 5900, a file generation tool may be used toencapsulate and package the compressed and/or uncompressed video, audioand/or associated parameters into a single file container. One examplewill be shown and described in connection with FIG. 62A.

FIG. 63A depicts a file generation tool 6300, according to oneembodiment. The file generation tool 6300 may be used to combine variousbits of data, such as the content parameter data 5970, captured scenedata 5960, vantages 6036, and audio content 6310. The file generationtool 6300 may have various encoding modules that encode and/or generateportions of the bitstream. By way of example, the file generation tool6300 may have one or more parameter format encoders 6320 that encode thecontent parameter data 5970 and/or the captured scene data 5960, one ormore video encoders 6322 that encode the vantages 6036, one or more tileaddress table builders 6324 that generate offset tables, such as thetile address offset table 6032, and one or more audio encoders 6326 thatencode the audio content 6310.

A file encapsulator/writer 6328 may combine the output of the parameterformat encoders 6320, the video encoders 6322, the tile address tablebuilders 6324, and/or the audio encoders 6326. The fileencapsulator/writer 6328 may thus generate the file 5900 (also called aLytro Virtual Reality File Format file (“LVR file”, or simply “LVR”).

A file generation tool may have a merging feature that combines multiplefiles like the file 5900. The files 5900 may have different GOPpartitions of a video stream, different payload types, or the like. Forexample, a user might merge a file 5900 containing only a single audiopayload 5980 with a file 5900 containing only a single video payload5990. One example will be shown and described in connection with FIG.63B.

FIG. 63B depicts a file generation tool 6350, according to anotherembodiment. The file generation tool 6350 may be used to generate videostreams for a plurality of GOPs 6016 in parallel, and then combine theminto a file 5900. Specifically, the file generation tool 6350 may have aplurality of video encoders 6322, with one video encoder 6322 for eachof the GOPs 6016. Each video encoder 6322 may encode the associated GOP6016, and the resulting video data may be written to a file 5900 by afile encapsulator/writer 6328. The files 5900 generated by the fileencapsulator/writers 6328 may be combined into a single file 5900 by amerger 6360.

Such a file generation process may reduce processing time by making thevideo encoding process scalable and/or parallelizable by breaking downthe video stream into multiple GOPs 6016. The GOPs 6016 may be encodedat the same time and merged into a single coherent video stream.Additionally or alternatively, similar parallelization techniques may bemade to audio payload and other medium payloads.

Variations

In alternative embodiments, more flexibilities may be provided in acompressed video payload stream (for example, the video payload 5990). Adifference codec may be used to compress different tiles across vantagesand across time within a GOP. Further, different compression parameters,such as block size, quality factors and adaptive Huffman coding, may beapplied individually to each tile. Mechanisms may be provided to supportinter-vantage prediction in addition to the ability to replaceduplicative tiles with offset addresses for similar tiles. Colorchannels and depth channels may be encoded separately. Auxiliarychannels, such as alpha map for AA, specular depth, and/or validity map,may also be supported and encoded into the file 5900. Thesemodifications may be signaled, for example, by introducing additionalheader data into the tile stream within the video payload 5990.

The above description and referenced drawings set forth particulardetails with respect to possible embodiments. Those of skill in the artwill appreciate that the techniques described herein may be practiced inother embodiments. First, the particular naming of the components,capitalization of terms, the attributes, data structures, or any otherprogramming or structural aspect is not mandatory or significant, andthe mechanisms that implement the techniques described herein may havedifferent names, formats, or protocols. Further, the system may beimplemented via a combination of hardware and software, as described, orentirely in hardware elements, or entirely in software elements. Also,the particular division of functionality between the various systemcomponents described herein is merely exemplary, and not mandatory;functions performed by a single system component may instead beperformed by multiple components, and functions performed by multiplecomponents may instead be performed by a single component.

Reference in the specification to “one embodiment” or to “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiments is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

Some embodiments may include a system or a method for performing theabove-described techniques, either singly or in any combination. Otherembodiments may include a computer program product comprising anon-transitory computer-readable storage medium and computer programcode, encoded on the medium, for causing a processor in a computingdevice or other electronic device to perform the above-describedtechniques.

Some portions of the above are presented in terms of algorithms andsymbolic representations of operations on data bits within a memory of acomputing device. These algorithmic descriptions and representations arethe means used by those skilled in the data processing arts to mosteffectively convey the substance of their work to others skilled in theart. An algorithm is here, and generally, conceived to be aself-consistent sequence of steps (instructions) leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical, magnetic or optical signals capable of being stored,transferred, combined, compared and otherwise manipulated. It isconvenient at times, principally for reasons of common usage, to referto these signals as bits, values, elements, symbols, characters, terms,numbers, or the like. Furthermore, it is also convenient at times, torefer to certain arrangements of steps requiring physical manipulationsof physical quantities as modules or code devices, without loss ofgenerality.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“displaying” or “determining” or the like, refer to the action andprocesses of a computer system, or similar electronic computing moduleand/or device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system memories orregisters or other such information storage, transmission or displaydevices.

Certain aspects include process steps and instructions described hereinin the form of an algorithm. It should be noted that the process stepsand instructions of described herein can be embodied in software,firmware and/or hardware, and when embodied in software, can bedownloaded to reside on and be operated from different platforms used bya variety of operating systems.

Some embodiments relate to an apparatus for performing the operationsdescribed herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computing deviceselectively activated or reconfigured by a computer program stored inthe computing device. Such a computer program may be stored in acomputer readable storage medium, such as, but is not limited to, anytype of disk including floppy disks, optical disks, CD-ROMs,magnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, flash memory, solid state drives,magnetic or optical cards, application specific integrated circuits(ASICs), and/or any type of media suitable for storing electronicinstructions, and each coupled to a computer system bus. Further, thecomputing devices referred to herein may include a single processor ormay be architectures employing multiple processor designs for increasedcomputing capability.

The algorithms and displays presented herein are not inherently relatedto any particular computing device, virtualized system, or otherapparatus. Various general-purpose systems may also be used withprograms in accordance with the teachings herein, or it may proveconvenient to construct more specialized apparatus to perform therequired method steps. The required structure for a variety of thesesystems will be apparent from the description provided herein. Inaddition, the techniques set forth herein are not described withreference to any particular programming language. It will be appreciatedthat a variety of programming languages may be used to implement thetechniques described herein, and any references above to specificlanguages are provided for illustrative purposes only.

Accordingly, in various embodiments, the techniques described herein canbe implemented as software, hardware, and/or other elements forcontrolling a computer system, computing device, or other electronicdevice, or any combination or plurality thereof. Such an electronicdevice can include, for example, a processor, an input device (such as akeyboard, mouse, touchpad, trackpad, joystick, trackball, microphone,and/or any combination thereof), an output device (such as a screen,speaker, and/or the like), memory, long-term storage (such as magneticstorage, optical storage, and/or the like), and/or network connectivity,according to techniques that are well known in the art. Such anelectronic device may be portable or nonportable. Examples of electronicdevices that may be used for implementing the techniques describedherein include: a mobile phone, personal digital assistant, smartphone,kiosk, server computer, enterprise computing device, desktop computer,laptop computer, tablet computer, consumer electronic device,television, set-top box, or the like. An electronic device forimplementing the techniques described herein may use any operatingsystem such as, for example: Linux; Microsoft Windows, available fromMicrosoft Corporation of Redmond, Wash.; Mac OS X, available from AppleInc. of Cupertino, Calif.; iOS, available from Apple Inc. of Cupertino,Calif.; Android, available from Google, Inc. of Mountain View, Calif.;and/or any other operating system that is adapted for use on the device.

In various embodiments, the techniques described herein can beimplemented in a distributed processing environment, networked computingenvironment, or web-based computing environment. Elements can beimplemented on client computing devices, servers, routers, and/or othernetwork or non-network components. In some embodiments, the techniquesdescribed herein are implemented using a client/server architecture,wherein some components are implemented on one or more client computingdevices and other components are implemented on one or more servers. Inone embodiment, in the course of implementing the techniques of thepresent disclosure, client(s) request content from server(s), andserver(s) return content in response to the requests. A browser may beinstalled at the client computing device for enabling such requests andresponses, and for providing a user interface by which the user caninitiate and control such interactions and view the presented content.

Any or all of the network components for implementing the describedtechnology may, in some embodiments, be communicatively coupled with oneanother using any suitable electronic network, whether wired or wirelessor any combination thereof, and using any suitable protocols forenabling such communication. One example of such a network is theInternet, although the techniques described herein can be implementedusing other networks as well.

While a limited number of embodiments has been described herein, thoseskilled in the art, having benefit of the above description, willappreciate that other embodiments may be devised which do not departfrom the scope of the claims. In addition, it should be noted that thelanguage used in the specification has been principally selected forreadability and instructional purposes, and may not have been selectedto delineate or circumscribe the inventive subject matter. Accordingly,the disclosure is intended to be illustrative, but not limiting.

What is claimed is:
 1. A method for storing a video stream of a scenefor a virtual reality or augmented reality experience, the methodcomprising: at one or more image capture devices, capturing the videostream; at a processor, dividing the video stream into a plurality ofunits based on at least a first selection from the group consisting oftime segmentation, viewpoint segmentation, and view orientationsegmentation, wherein dividing the video stream into the plurality ofunits comprises dividing the video stream into the plurality of unitsbased on time segmentation and each of the units comprises a group ofpictures comprising a sequence of successive frames in time; at theprocessor, dividing each of the units into a plurality of sub-units,based on at least a second selection, different from the firstselection, from the group consisting of time segmentation, viewpointsegmentation, and view orientation segmentation, wherein dividing eachof the units into the plurality of sub-units comprises dividing each ofthe groups of pictures into the plurality of sub-units based onviewpoint segmentation and each of the sub-units comprises a vantagedefining a viewpoint from which the scene is viewable; at a data store,storing at least a portion of the video stream in a file comprising aplurality of the units; at the processor, based on a viewer positionand/or orientation, identifying a subset of the file; from the datastore, retrieving the subset of the file without retrieving a remainderof the file; at the processor, using the subset to generate a view ofthe scene from the viewer position and/or orientation; on a displaydevice, displaying the view; and dividing each of the vantages into aplurality of sub-sub-units, based on at least a third selection,different from the first selection and the second selection, from thegroup consisting of time segmentation, viewpoint segmentation, and vieworientation segmentation.
 2. The method of claim 1, further comprising:at the processor, generating a unit offset table indicating a startaddress, within the data store, of each of the units; and at the datastore, storing the unit offset table in the file.
 3. The method of claim2, further comprising: at the processor, generating a sub-unit offsettable indicating a start address, within the data store, of each of thesub-units; and at the data store, storing the sub-unit offset table inthe file.
 4. The method of claim 1, wherein: dividing each of thevantages into the plurality of sub-sub-units comprises dividing each ofthe groups of pictures into the plurality of sub-units based on vieworientation segmentation; and each of the sub-sub-units comprises a tilecomprising part of one of the vantages, limited to one or moreparticular view orientations along which the scene is viewable from theone of the vantages.
 5. The method of claim 4, further comprisingdividing each of the vantages into frames; wherein dividing each of thevantages into a plurality of sub-sub-units comprises dividing each ofthe frames into the tiles.
 6. The method of claim 4, further comprising:at the processor, generating a unit offset table indicating a startaddress, within the data store, of each of the groups of pictures; atthe processor, generating a sub-unit offset table indicating a startaddress, within the data store, of each of the vantages; at theprocessor, generating a sub-sub-unit offset table indicating a startaddress, within the data store, of each of the tiles; and at the datastore, storing the unit offset table, the sub-unit offset table, and thesub-sub-unit offset table in the file.
 7. The method of claim 4, furthercomprising identifying at least one duplicate tile of the tiles that issubstantially duplicative of a base tile of the tiles; wherein storingat least the portion of the video stream in the file comprises storing,in place of the duplicate tile, a start address, within the data store,at which the base tile is stored.
 8. The method of claim 1, furthercomprising, at the data store, storing captured scene data comprising atleast one of: scene configuration information pertinent to the scene;rendering information defining how the video stream is to be renderedfor a viewer; and vantage parameters pertinent to a plurality ofvantages, each of which defines a viewpoint from which the scene isviewable.
 9. The method of claim 1, further comprising, at the datastore, storing content parameters comprising at least one of: gray roomtransition defining how transitions in viewpoints are handled duringviewing of the video stream; viewing volume behavior of a viewing volumefrom which the scene is viewable; and viewing volume rotation and/oroffset defining rotation and/or position of the viewing volume.
 10. Themethod of claim 1, further comprising: at the data store, storing atleast a second portion of the video stream in a second file comprising asecond plurality of the units; and at the processor, combining the filewith the second file to generate a combined file comprising the portionand the second portion of the video stream.
 11. A non-transitorycomputer-readable medium for storing a video stream of a scene for avirtual reality or augmented reality experience, comprising instructionsstored thereon, that when executed by one or more processors, performthe steps of: causing a data store to retrieve a video stream capturedby one or more image capture devices; dividing the video stream into aplurality of units based on at least a first selection from the groupconsisting of time segmentation, viewpoint segmentation, and vieworientation segmentation, wherein dividing the video stream into theplurality of units comprises dividing the video stream into theplurality of units based on time segmentation and each of the unitscomprises a group of pictures comprising a sequence of successive framesin time; dividing each of the units into a plurality of sub-units, basedon at least a second selection, different from the first selection, fromthe group consisting of time segmentation, viewpoint segmentation, andview orientation segmentation, wherein dividing each of the units intothe plurality of sub-units comprises dividing each of the groups ofpictures into the plurality of sub-units based on viewpointsegmentation, and each of the sub-units comprises a vantage defining aviewpoint from which the scene is viewable; causing the data store tostore at least a portion of the video stream in a file comprising aplurality of the units; based on a viewer position and/or orientation,identifying a subset of the file; retrieving the subset of the filewithout retrieving a remainder of the file; using the subset to generatea view of the scene from the viewer position and/or orientation; anddisplaying the view on a display device, wherein: the non-transitorycomputer-readable medium further comprises instructions stored thereon,that when executed by one or more processors, divide each of thevantages into a plurality of sub-sub-units, based on at least a thirdselection, different from the first selection and the second selection,from the group consisting of time segmentation, viewpoint segmentation,and view orientation segmentation; dividing each of the vantages intothe plurality of sub-sub-units comprises dividing each of the groups ofpictures into the plurality of sub-units based on view orientationsegmentation; and each of the sub-sub-units comprises a tile comprisingpart of one of the vantages, limited to one or more particular vieworientations along which the scene is viewable from the one of thevantages.
 12. The non-transitory computer-readable medium of claim 11,further comprising instructions stored thereon, that when executed byone or more processors, perform the steps of: generating a unit offsettable indicating a start address, within the data store, of each of theunits; causing the data store to store the unit offset table in thefile; generating a sub-unit offset table indicating a start address,within the data store, of each of the sub-units; and causing the datastore to store the sub-unit offset table in the file.
 13. Thenon-transitory computer-readable medium of claim 11, further comprisinginstructions stored thereon, that when executed by one or moreprocessors, divide each of the vantages into frames; wherein dividingeach of the vantages into a plurality of sub-sub-units comprisesdividing each of the frames into the tiles.
 14. The non-transitorycomputer-readable medium of claim 11, further comprising instructionsstored thereon, that when executed by one or more processors, performthe steps of: generating a unit offset table indicating a start address,within the data store, of each of the groups of pictures; generating asub-unit offset table indicating a start address, within the data store,of each of the vantages; and generating a sub-sub-unit offset tableindicating a start address, within the data store, of each of the tiles;and causing the data store to store the unit offset table, the sub-unitoffset table, and the sub-sub-unit offset table in the file.
 15. Thenon-transitory computer-readable medium of claim 11, further comprisinginstructions stored thereon, that when executed by one or moreprocessors, identify at least one duplicate tile of the tiles that issubstantially duplicative of a base tile of the tiles; wherein storingat least the portion of the video stream in the file comprises storing,in place of the duplicate tile, a start address, within the data store,at which the base tile is stored.
 16. The non-transitorycomputer-readable medium of claim 11, further comprising instructionsstored thereon, that when executed by one or more processors, performthe steps of: causing the data store to store at least a second portionof the video stream in a second file comprising a second plurality ofthe units; and combining the file with the second file to generate acombined file comprising the portion and the second portion of the videostream.
 17. A system for storing a video stream of a scene for a virtualreality or augmented reality experience, the system comprising: aprocessor configured to: divide a video stream captured by one or moreimage capture devices into a plurality of units based on at least afirst selection from the group consisting of time segmentation,viewpoint segmentation, and view orientation segmentation; and divideeach of the units into a plurality of sub-units, based on at least asecond selection, different from the first selection, from the groupconsisting of time segmentation, viewpoint segmentation, and vieworientation segmentation; a data store configured to store at least aportion of the video stream in a file comprising a plurality of theunits; and a display device; wherein the processor is further configuredto: based on a viewer position and/or orientation, identify a subset ofthe file; retrieve the subset of the file without retrieving a remainderof the file; and use the subset to generate a view of the scene from theviewer position and/or orientation; wherein the processor is furtherconfigured to divide the video stream into the plurality of units bydividing the video stream into the plurality of units based on timesegmentation, and each of the units comprises a group of picturescomprising a sequence of successive frames in time; the processor isfurther configured to divide each of the units into the plurality ofsub-units by dividing each of the groups of pictures into the pluralityof sub-units based on viewpoint segmentation, and each of the sub-unitscomprises a vantage defining a viewpoint from which the scene isviewable; wherein the processor is further configured to divide each ofthe vantages into a plurality of sub-sub-units, based on at least athird selection, different from the first selection and the secondselection, from the group consisting of time segmentation, viewpointsegmentation, and view orientation segmentation; wherein the processoris further configured to divide each of the vantages into the pluralityof sub-sub-units by dividing each of the groups of pictures into theplurality of sub-units based on view orientation segmentation; each ofthe sub-sub-units comprises a tile comprising part of one of thevantages, limited to one or more particular view orientations alongwhich the scene is viewable from the one of the vantages; and whereinthe display device is further configured to display the view.
 18. Thesystem of claim 17, wherein: the processor is further configured togenerate a unit offset table indicating a start address, within the datastore, of each of the units; the data store is further configured tostore the unit offset table in the file the processor is furtherconfigured to generate a sub-unit offset table indicating a startaddress, within the data store, of each of the sub-units; and the datastore is further configured to store the sub-unit offset table in thefile.
 19. The system of claim 17, wherein: the processor is furtherconfigured to divide each of the vantages into frames; and the processoris further configured to divide each of the vantages into a plurality ofsub-sub-units by dividing each of the frames into the tiles.
 20. Thesystem of claim 17, wherein: the processor is further configured to:generate a unit offset table indicating a start address, within the datastore, of each of the groups of pictures; generate a sub-unit offsettable indicating a start address, within the data store, of each of thevantages; and generate a sub-sub-unit offset table indicating a startaddress, within the data store, of each of the tiles; and the data storeis further configured to store the unit offset table, the sub-unitoffset table, and the sub-sub-unit offset table in the file.
 21. Thesystem of claim 17, wherein: the processor is further configured toidentify at least one duplicate tile of the tiles that is substantiallyduplicative of a base tile of the tiles; and the data store is furtherconfigured to store at least the portion of the video stream in the fileby storing, in place of the duplicate tile, a start address, within thedata store, at which the base tile is stored.
 22. The system of claim17, wherein: the data store is further configured to store at least asecond portion of the video stream in a second file comprising a secondplurality of the units; and the processor is further configured tocombine the file with the second file to generate a combined filecomprising the portion and the second portion of the video stream. 23.The system of claim 17, further comprising the one or more image capturedevices.